0.1 Carga de paquetes

Son muchos los paquetes empleados en estos análisis. Puedes consultar en el ChatGPT qué hace cada uno. Considera un aspecto también importante: algunas funciones escritas por mí se cargan con source_url y source; dentro de algunas de dichas funciones, también se cargan paquetes adicionales.

library(vegan)
library(sf)
library(tidyverse)
library(tmap)
library(kableExtra)
library(broom)
library(cluster)
library(gclus)
library(pvclust)
library(foreach)
library(leaps)
library(caret)
library(RColorBrewer)
library(indicspecies)
library(dendextend)
library(adespatial)
library(SpadeR)
library(iNEXT)
library(GGally)
library(vegetarian)

gh_content <- 'https://raw.githubusercontent.com/'
gh_zonal_stats <- paste0(gh_content,
                         'geofis/zonal-statistics/0b2e95aaee87bf326cf132d28f4bd15220bb4ec7/out/')
repo_analisis <- 'biogeografia-master/scripts-de-analisis-BCI/master'
repo_sem202202 <- 'biogeografia-202202/material-de-apoyo/master/practicas/'
devtools::source_url(paste0(gh_content, repo_analisis, '/biodata/funciones.R'))
devtools::source_url(paste0(gh_content, repo_sem202202, 'train.R'))
devtools::source_url(paste0(gh_content, repo_sem202202, 'funciones.R'))
source('R/funciones.R')
source('matrices-de-comunidad-y-ambiental.R')
umbral_alfa <- 0.05

1 Análisis exploratorio de datos (AED)

1.1 Cargar la matriz de comunidad

todos_los_objetos <- readRDS('salidas_RDS/todos_los_objetos_Acanthaceae.RDS')
mc <- todos_los_objetos$mc
mc %>% estilo_kable(
  titulo = 'Matriz de comunidad',
  nombres_filas = T, alinear = 'r')

Table 1.1: Matriz de comunidad
	Thunbergia grandiflora (Roxb. ex Rottler) Roxb.	Thunbergia alata Bojer ex Sims	Pachystachys lutea (Ruiz & Pav. ex Schult.) Nees	Odontonema cuspidatum (Nees) Kuntze	Megaskepasma erythrochlamys Lindau	Asystasia gangetica (L.) T.Anderson	Ruellia simplex C.Wright	Ruellia ciliatiflora Hook.	Ruellia tuberosa L.	Ruellia coccinea (L.) Vahl	Ruellia blechum L.	Pachystachys spicata (Ruiz & Pav.) Wassh.	Barleriola solanifolia (L.) Oerst. ex Lindau	Oplonia spinosa (Jacq.) Raf.	Dianthera secunda (Lam.) Griseb.	Thunbergia fragrans Roxb.	Avicennia germinans (L.) L.	Barleria lupulina Lindl.	Andrographis paniculata (Burm.fil.) Nees	Dianthera pectoralis (Jacq.) J.F.Gmel.	Sanchezia parvibracteata Sprague & Hutch.	Crossandra infundibuliformis (L.) Nees	Graptophyllum pictum (L.) Griff.	Thunbergia erecta (Benth.) T.Anderson	Justicia brandegeeana Wassh. & L.B.Sm.	Dicliptera mucronata Urb.	Oplonia microphylla (Lam.) Stearn	Ruellia domingensis Spreng. ex Nees	Justicia disparifolia Urb. & Ekman	Barleriola inermis Urb. & Ekman	Dyschoriste diffusa (Nees) Urb.	Lepidagathis alopecuroidea (Vahl) R.Br. ex Griseb.	Hygrophila costata Nees	Justicia alsinoides Leonard	Justicia mirabiloides Lam.	Ruellia lepidota Urb.	Dianthera sessilis (Jacq.) J.F.Gmel.	Stenandrium tuberosum (L.) Urb.	Ruellia geminiflora Kunth	Dianthera reptans (Sw.) J.F.Gmel.	Oplonia acicularis (Sw.) Stearn
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854cd66ffffffff	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
854cd697fffffff	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
854cd6affffffff	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	1	0	0	0	0	0	0	0	0	0	0
854cd58bfffffff	0	1	0	0	0	0	0	0	1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
854cd44bfffffff	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0

Puede ser que sea un poco inabarcable la matriz, porque podría tener muchas especies, en cuyo caso te recomiendo que selecciones columnas al azar y especifique se trata de un extracto.

set.seed(99); mc[, sample(1:ncol(mc), 10)] %>% estilo_kable(
  titulo = 'Matriz de comunidad',
  nombres_filas = T, alinear = 'r')

Table 1.2: Matriz de comunidad
	Hygrophila costata Nees	Crossandra infundibuliformis (L.) Nees	Justicia mirabiloides Lam.	Justicia alsinoides Leonard	Lepidagathis alopecuroidea (Vahl) R.Br. ex Griseb.	Barleriola solanifolia (L.) Oerst. ex Lindau	Dianthera pectoralis (Jacq.) J.F.Gmel.	Dyschoriste diffusa (Nees) Urb.	Odontonema cuspidatum (Nees) Kuntze	Ruellia tuberosa L.
854c8997fffffff	0	0	0	0	0	0	0	0	0	0
854cd29bfffffff	0	0	0	0	0	1	1	0	1	0
854cd477fffffff	0	1	0	0	0	0	0	0	0	0
854cd46bfffffff	0	0	0	0	0	0	0	1	0	0
854c8927fffffff	0	1	0	0	0	0	0	0	0	1
854c89b3fffffff	0	0	0	0	0	0	0	0	1	1
854cd4cbfffffff	1	1	0	0	0	0	0	0	1	0
854cd40bfffffff	0	0	0	0	1	0	0	0	1	0
854cd423fffffff	0	0	0	0	0	0	0	0	0	0
854cc6c7fffffff	0	1	0	0	0	0	0	0	0	1
854c892ffffffff	1	0	0	0	0	0	0	0	1	0
854cd63bfffffff	0	0	0	0	0	0	0	0	0	0
854cd457fffffff	1	0	0	0	0	0	0	0	0	0
856725b7fffffff	0	0	0	0	0	1	0	1	0	0
854cf26ffffffff	0	0	0	0	0	0	0	0	0	1
85672537fffffff	0	0	0	0	0	0	0	0	0	1
854c89abfffffff	0	0	0	0	0	0	0	0	0	0
854c89a3fffffff	0	0	0	0	0	0	0	0	1	1
854cd647fffffff	0	0	0	0	0	0	0	0	0	1
854cd453fffffff	0	0	0	1	1	0	0	0	1	0
854cf373fffffff	0	0	0	0	0	0	0	0	1	0
854c89c7fffffff	0	0	0	0	0	0	0	0	0	1
854c89c3fffffff	0	0	0	0	0	0	0	1	0	0
854cf31bfffffff	0	0	0	0	0	0	0	0	0	0
854cf243fffffff	0	0	0	0	0	0	1	0	0	0
854cd0c3fffffff	0	0	0	0	0	0	0	0	0	0
854cd42ffffffff	0	1	0	0	0	0	0	0	0	1
854cf347fffffff	0	0	1	0	0	0	0	0	0	0
854cf303fffffff	0	0	0	0	0	0	0	0	0	0
854cd5b3fffffff	0	0	0	0	0	0	0	0	0	1
854cc64ffffffff	0	1	0	0	0	0	0	0	0	0
854c893bfffffff	0	0	0	0	0	0	0	0	0	0
854cf267fffffff	0	0	0	0	1	0	0	0	0	0
854cf333fffffff	0	0	0	0	0	0	0	0	0	0
854cf26bfffffff	0	0	0	0	0	0	0	0	0	0
854cd427fffffff	0	0	0	0	0	1	0	0	0	0
854c894bfffffff	0	0	0	0	0	0	0	0	0	0
854cf20ffffffff	0	0	0	0	0	0	0	0	0	1
854cc6cffffffff	0	0	0	0	0	0	0	0	0	0
854cc603fffffff	0	0	0	0	0	0	0	0	0	0
854cc613fffffff	0	0	0	0	0	0	0	0	0	1
854cd513fffffff	0	0	0	0	0	0	0	0	0	0
854cd5b7fffffff	0	1	0	0	0	1	0	0	0	1
854cf353fffffff	0	0	0	0	0	0	0	0	0	0
854cd403fffffff	0	0	0	0	0	0	0	0	0	0
854cd093fffffff	0	0	0	0	0	0	0	0	0	1
854c89a7fffffff	0	0	0	0	0	0	0	0	0	1
854cd553fffffff	0	0	0	0	0	0	0	0	0	1
854cf24bfffffff	0	0	0	0	0	0	0	0	0	0
854cd09bfffffff	0	0	0	0	0	0	0	0	1	0
854cd653fffffff	0	0	0	0	0	0	0	1	0	0
854cd46ffffffff	0	0	0	0	0	0	0	0	1	0
854cd4d3fffffff	0	1	0	0	0	0	0	0	0	0
854cc657fffffff	0	0	0	0	1	0	0	0	0	0
854cd41bfffffff	0	0	0	0	0	0	0	0	1	0
854cd293fffffff	0	0	0	0	0	0	0	0	0	0
854c8933fffffff	0	0	0	0	0	0	0	1	0	0
854cd667fffffff	0	0	0	0	0	1	0	0	0	0
854c898ffffffff	0	0	0	0	0	0	0	1	0	1
854cd2d7fffffff	0	0	0	0	0	0	0	0	0	0
854c89bbfffffff	0	0	0	0	0	0	0	0	0	1
854cd40ffffffff	0	0	0	0	0	0	0	0	1	0
854cc67bfffffff	0	0	1	0	0	0	0	0	0	0
854cd467fffffff	0	0	0	0	0	0	0	0	1	0
854cf323fffffff	0	0	0	0	0	0	0	0	1	0
854c89b7fffffff	0	0	0	0	1	0	0	0	0	0
854cd5cffffffff	0	0	0	0	0	0	0	0	0	0
854cd693fffffff	0	0	0	1	0	0	0	0	0	0
854cf343fffffff	1	0	0	0	1	0	0	0	0	0
854cd47bfffffff	0	0	0	0	0	0	0	0	1	0
854cd623fffffff	0	0	0	0	0	0	0	0	0	0
854cd473fffffff	1	0	0	0	0	0	0	0	1	0
854c890ffffffff	0	0	0	0	0	0	0	0	0	0
854cd4c3fffffff	0	0	0	0	0	0	0	0	0	0
854cc673fffffff	0	0	0	0	0	0	0	0	0	0
854cd66ffffffff	0	0	0	0	0	0	0	0	0	1
854cd697fffffff	1	0	0	0	0	0	0	0	0	0
854cd6affffffff	0	0	0	0	0	0	0	1	0	0
854cd58bfffffff	0	0	0	0	0	0	0	0	0	1
854cd44bfffffff	0	0	0	0	0	0	0	0	0	0

Esta es la lista de especies.

data.frame(Especies = sort(names(mc))) %>%
  estilo_kable(titulo = 'Lista de especies', cubre_anchura = F, alinear = 'c') %>% 
  column_spec(column = 1, width = "15em")

Table 1.3: Lista de especies
Especies
Andrographis paniculata (Burm.fil.) Nees
Asystasia gangetica (L.) T.Anderson
Avicennia germinans (L.) L.
Barleria lupulina Lindl.
Barleriola inermis Urb. & Ekman
Barleriola solanifolia (L.) Oerst. ex Lindau
Crossandra infundibuliformis (L.) Nees
Dianthera pectoralis (Jacq.) J.F.Gmel.
Dianthera reptans (Sw.) J.F.Gmel.
Dianthera secunda (Lam.) Griseb.
Dianthera sessilis (Jacq.) J.F.Gmel.
Dicliptera mucronata Urb.
Dyschoriste diffusa (Nees) Urb.
Graptophyllum pictum (L.) Griff.
Hygrophila costata Nees
Justicia alsinoides Leonard
Justicia brandegeeana Wassh. & L.B.Sm.
Justicia disparifolia Urb. & Ekman
Justicia mirabiloides Lam.
Lepidagathis alopecuroidea (Vahl) R.Br. ex Griseb.
Megaskepasma erythrochlamys Lindau
Odontonema cuspidatum (Nees) Kuntze
Oplonia acicularis (Sw.) Stearn
Oplonia microphylla (Lam.) Stearn
Oplonia spinosa (Jacq.) Raf.
Pachystachys lutea (Ruiz & Pav. ex Schult.) Nees
Pachystachys spicata (Ruiz & Pav.) Wassh.
Ruellia blechum L.
Ruellia ciliatiflora Hook.
Ruellia coccinea (L.) Vahl
Ruellia domingensis Spreng. ex Nees
Ruellia geminiflora Kunth
Ruellia lepidota Urb.
Ruellia simplex C.Wright
Ruellia tuberosa L.
Sanchezia parvibracteata Sprague & Hutch.
Stenandrium tuberosum (L.) Urb.
Thunbergia alata Bojer ex Sims
Thunbergia erecta (Benth.) T.Anderson
Thunbergia fragrans Roxb.
Thunbergia grandiflora (Roxb. ex Rottler) Roxb.

Y esta el número de sitios (hexágonos H3) donde fue reportada cada especie

data.frame(
  `Número de sitios donde fue reportada cada especie` = sort(colSums(mc), decreasing = T),
           check.names = F) %>%
  rownames_to_column('Especie') %>% 
  estilo_kable(
    titulo = 'Número de sitios en los que está presente cada especie (orden descendente por número de sitios)', 
    nombres_filas = F, alinear = 'cr')

Table 1.4: Número de sitios en los que está presente cada especie (orden descendente por número de sitios)
Especie	Número de sitios donde fue reportada cada especie
Thunbergia alata Bojer ex Sims	27
Ruellia simplex C.Wright	23
Ruellia tuberosa L.	20
Thunbergia grandiflora (Roxb. ex Rottler) Roxb.	19
Odontonema cuspidatum (Nees) Kuntze	16
Thunbergia fragrans Roxb.	16
Asystasia gangetica (L.) T.Anderson	14
Ruellia blechum L.	13
Ruellia coccinea (L.) Vahl	9
Megaskepasma erythrochlamys Lindau	8
Oplonia spinosa (Jacq.) Raf.	8
Avicennia germinans (L.) L.	8
Crossandra infundibuliformis (L.) Nees	8
Barleria lupulina Lindl.	7
Dyschoriste diffusa (Nees) Urb.	7
Pachystachys lutea (Ruiz & Pav. ex Schult.) Nees	6
Ruellia domingensis Spreng. ex Nees	6
Lepidagathis alopecuroidea (Vahl) R.Br. ex Griseb.	6
Hygrophila costata Nees	6
Stenandrium tuberosum (L.) Urb.	6
Barleriola solanifolia (L.) Oerst. ex Lindau	5
Graptophyllum pictum (L.) Griff.	5
Oplonia microphylla (Lam.) Stearn	5
Justicia disparifolia Urb. & Ekman	5
Justicia brandegeeana Wassh. & L.B.Sm.	4
Ruellia ciliatiflora Hook.	3
Pachystachys spicata (Ruiz & Pav.) Wassh.	3
Sanchezia parvibracteata Sprague & Hutch.	3
Barleriola inermis Urb. & Ekman	3
Ruellia lepidota Urb.	3
Dianthera secunda (Lam.) Griseb.	2
Andrographis paniculata (Burm.fil.) Nees	2
Dianthera pectoralis (Jacq.) J.F.Gmel.	2
Dicliptera mucronata Urb.	2
Justicia alsinoides Leonard	2
Justicia mirabiloides Lam.	2
Dianthera sessilis (Jacq.) J.F.Gmel.	2
Dianthera reptans (Sw.) J.F.Gmel.	2
Thunbergia erecta (Benth.) T.Anderson	1
Ruellia geminiflora Kunth	1
Oplonia acicularis (Sw.) Stearn	1

data.frame(`Riqueza por sitios` = rowSums(mc),
           check.names = F) %>%  rownames_to_column('Sitio') %>% 
  arrange(desc(`Riqueza por sitios`)) %>% 
  estilo_kable(
    titulo = 'Riqueza por sitios (orden descendente por riqueza)', 
    nombres_filas = F, alinear = 'cr')

Table 1.5: Riqueza por sitios (orden descendente por riqueza)
Sitio	Riqueza por sitios
854c8997fffffff	11
854cf243fffffff	11
854cd4cbfffffff	10
854cd42ffffffff	10
854cd427fffffff	8
854c898ffffffff	8
854cd453fffffff	7
854cd5b7fffffff	7
854c8927fffffff	6
854cd423fffffff	6
854cf26ffffffff	6
854c89a3fffffff	6
854cd29bfffffff	5
854c89b3fffffff	5
854cd457fffffff	5
854cf31bfffffff	5
854cd667fffffff	5
854cd477fffffff	4
854cd40bfffffff	4
854c892ffffffff	4
856725b7fffffff	4
854cd5b3fffffff	4
854cc6cffffffff	4
854cc613fffffff	4
854cd513fffffff	4
854cd09bfffffff	4
854cd467fffffff	4
854cc6c7fffffff	3
854c89abfffffff	3
854cf373fffffff	3
854c89c3fffffff	3
854cf347fffffff	3
854cf303fffffff	3
854c893bfffffff	3
854cf20ffffffff	3
854cc603fffffff	3
854c89a7fffffff	3
854cf24bfffffff	3
854cd46ffffffff	3
854cd4d3fffffff	3
854cc657fffffff	3
854cd41bfffffff	3
854cd293fffffff	3
854c8933fffffff	3
854cd2d7fffffff	3
854cf323fffffff	3
854cf343fffffff	3
854cd623fffffff	3
854cd473fffffff	3
854cd697fffffff	3
854cd58bfffffff	3
854cd46bfffffff	2
854cd63bfffffff	2
85672537fffffff	2
854cd647fffffff	2
854c89c7fffffff	2
854cd0c3fffffff	2
854cc64ffffffff	2
854cf267fffffff	2
854cf333fffffff	2
854cf26bfffffff	2
854c894bfffffff	2
854cf353fffffff	2
854cd403fffffff	2
854cd093fffffff	2
854cd553fffffff	2
854cd653fffffff	2
854c89bbfffffff	2
854cd40ffffffff	2
854cc67bfffffff	2
854c89b7fffffff	2
854cd5cffffffff	2
854cd693fffffff	2
854cd47bfffffff	2
854c890ffffffff	2
854cd4c3fffffff	2
854cc673fffffff	2
854cd66ffffffff	2
854cd6affffffff	2
854cd44bfffffff	2

La matriz de comunidad analizada se compone de 80 sitios y 41 especies, donde el/los sitio/s más ricos es/son 854c8997fffffff y 854cf243fffffff. La/s especie/s más común/es es/son Thunbergia alata Bojer ex Sims y la/s más rara/s es/son Thunbergia erecta (Benth.) T.Anderson, Ruellia geminiflora Kunth y Oplonia acicularis (Sw.) Stearn. El siguiente gráfico de mosaicos muestra la distribución de las especies según sitios.

grafico_mosaico <- crear_grafico_mosaico_de_mc(mc, tam_rotulo = 8) + xlab('Sitios') + ylab('Especie')
grafico_mosaico

Figure 1.1: Distribución de las especies según sitios

Si los rótulos del gráfico se superpponen o son ilegiblesse empasta, se puede probar con

Puedes también generar otros gráficos. En todos_los_objetos se encuentran, como su nombre lo indica, todos los objetos creados por el toolchain del cuaderno matrices-de-comunidad-y-ambiental.Rmd que creé para ti. Uno de ellos consiste en un mapa de distribución de los registros de presencia de GBIF para el taxón, y los hexágonos de la biblioteca H3.

# Mapa
todos_los_objetos$aoi_reg_hex_inter_g

1.2 Transformar la matriz de comunidad

Este paso es importante, lo explico aquí

mc_t <- decostand(mc, 'hellinger') #Hellinger, funciona con datos de presencia/ausencia
mc_t %>% estilo_kable(titulo = 'Matriz de comunidad transformada',
                      nombres_filas = T, alinear = 'r')

Table 1.6: Matriz de comunidad transformada
	Thunbergia grandiflora (Roxb. ex Rottler) Roxb.	Thunbergia alata Bojer ex Sims	Pachystachys lutea (Ruiz & Pav. ex Schult.) Nees	Odontonema cuspidatum (Nees) Kuntze	Megaskepasma erythrochlamys Lindau	Asystasia gangetica (L.) T.Anderson	Ruellia simplex C.Wright	Ruellia ciliatiflora Hook.	Ruellia tuberosa L.	Ruellia coccinea (L.) Vahl	Ruellia blechum L.	Pachystachys spicata (Ruiz & Pav.) Wassh.	Barleriola solanifolia (L.) Oerst. ex Lindau	Oplonia spinosa (Jacq.) Raf.	Dianthera secunda (Lam.) Griseb.	Thunbergia fragrans Roxb.	Avicennia germinans (L.) L.	Barleria lupulina Lindl.	Andrographis paniculata (Burm.fil.) Nees	Dianthera pectoralis (Jacq.) J.F.Gmel.	Sanchezia parvibracteata Sprague & Hutch.	Crossandra infundibuliformis (L.) Nees	Graptophyllum pictum (L.) Griff.	Thunbergia erecta (Benth.) T.Anderson	Justicia brandegeeana Wassh. & L.B.Sm.	Dicliptera mucronata Urb.	Oplonia microphylla (Lam.) Stearn	Ruellia domingensis Spreng. ex Nees	Justicia disparifolia Urb. & Ekman	Barleriola inermis Urb. & Ekman	Dyschoriste diffusa (Nees) Urb.	Lepidagathis alopecuroidea (Vahl) R.Br. ex Griseb.	Hygrophila costata Nees	Justicia alsinoides Leonard	Justicia mirabiloides Lam.	Ruellia lepidota Urb.	Dianthera sessilis (Jacq.) J.F.Gmel.	Stenandrium tuberosum (L.) Urb.	Ruellia geminiflora Kunth	Dianthera reptans (Sw.) J.F.Gmel.	Oplonia acicularis (Sw.) Stearn
854c8997fffffff	0.30	0.30	0.30	0.00	0.30	0.30	0.30	0.30	0.00	0.00	0.00	0.00	0.00	0.30	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.30	0.00	0.30	0.00	0.30	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
854cd29bfffffff	0.00	0.00	0.00	0.45	0.00	0.00	0.00	0.00	0.00	0.45	0.00	0.00	0.45	0.00	0.00	0.00	0.00	0.00	0.00	0.45	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.45	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
854cd477fffffff	0.50	0.50	0.00	0.00	0.50	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.50	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
854cd46bfffffff	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.71	0.71	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
854c8927fffffff	0.00	0.41	0.41	0.00	0.00	0.00	0.00	0.00	0.41	0.00	0.00	0.00	0.00	0.00	0.00	0.41	0.00	0.00	0.00	0.00	0.00	0.41	0.00	0.00	0.00	0.00	0.00	0.00	0.41	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
854c89b3fffffff	0.00	0.45	0.00	0.45	0.00	0.00	0.00	0.00	0.45	0.00	0.00	0.00	0.00	0.00	0.45	0.45	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
854cd4cbfffffff	0.32	0.32	0.00	0.32	0.32	0.00	0.32	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.32	0.00	0.32	0.00	0.00	0.00	0.32	0.00	0.00	0.00	0.00	0.32	0.00	0.00	0.00	0.00	0.32	0.00	0.00	0.00
854cd40bfffffff	0.00	0.00	0.00	0.50	0.00	0.00	0.00	0.00	0.00	0.00	0.50	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.50	0.00	0.00	0.00	0.00	0.00	0.50	0.00	0.00	0.00
854cd423fffffff	0.00	0.41	0.00	0.00	0.00	0.00	0.41	0.00	0.00	0.00	0.00	0.00	0.00	0.41	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.41	0.41	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.41	0.00	0.00	0.00
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854cc657fffffff	0.00	0.58	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.58	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.58	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
854cd41bfffffff	0.00	0.58	0.00	0.58	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.58	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
854cd293fffffff	0.00	0.00	0.00	0.00	0.00	0.58	0.58	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.58	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
854c8933fffffff	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.58	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.58	0.00	0.00	0.58	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
854cd667fffffff	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.45	0.00	0.00	0.45	0.00	0.00	0.00	0.45	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.45	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.45
854c898ffffffff	0.00	0.35	0.00	0.00	0.00	0.00	0.00	0.00	0.35	0.00	0.35	0.00	0.00	0.35	0.00	0.35	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.35	0.00	0.00	0.35	0.00	0.00	0.00	0.00	0.00	0.35	0.00	0.00	0.00	0.00
854cd2d7fffffff	0.00	0.58	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.58	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.58	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
854c89bbfffffff	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.71	0.71	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
854cd40ffffffff	0.00	0.00	0.00	0.71	0.00	0.00	0.71	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
854cc67bfffffff	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.71	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.71	0.00	0.00	0.00	0.00	0.00	0.00
854cd467fffffff	0.00	0.50	0.00	0.50	0.00	0.00	0.00	0.00	0.00	0.50	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.50	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
854cf323fffffff	0.00	0.58	0.00	0.58	0.00	0.00	0.00	0.00	0.00	0.00	0.58	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
854c89b7fffffff	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.71	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.71	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
854cd5cffffffff	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.71	0.71	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
854cd693fffffff	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.71	0.00	0.00	0.00	0.00	0.71	0.00	0.00	0.00	0.00	0.00	0.00	0.00
854cf343fffffff	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.58	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.58	0.58	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
854cd47bfffffff	0.00	0.71	0.00	0.71	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
854cd623fffffff	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.58	0.58	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.58	0.00	0.00	0.00	0.00
854cd473fffffff	0.00	0.00	0.00	0.58	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.58	0.00	0.00	0.58	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
854c890ffffffff	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.71	0.00	0.71	0.00	0.00	0.00
854cd4c3fffffff	0.00	0.71	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.71	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
854cc673fffffff	0.00	0.71	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.71	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
854cd66ffffffff	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.71	0.00	0.71	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
854cd697fffffff	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.58	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.58	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.58	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
854cd6affffffff	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.71	0.00	0.00	0.71	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
854cd58bfffffff	0.00	0.58	0.00	0.00	0.00	0.00	0.00	0.00	0.58	0.00	0.58	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
854cd44bfffffff	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.71	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.71	0.00	0.00

# Otras transformaciones posibles con datos de presencia/ausencia
# mc_t <- decostand(mc, 'normalize') #Chord
# mc_t <- decostand(log1p(mc), 'normalize') #Chord
# mc_t <- decostand(mc, 'chi.square') #Chi-square

1.3 Cargar la matriz ambiental

env <- todos_los_objetos$ma
env %>% estilo_kable(titulo = 'Matriz ambiental', nombres_filas = T, alinear = 'r')

Table 1.7: Matriz ambiental
	ESA Trees	ESA Shrubland	ESA Grassland	ESA Cropland	ESA Built-up	ESA Barren / sparse vegetation	ESA Open water	ESA Herbaceous wetland	ESA Mangroves	CGL Closed forest, evergreen needle leaf	CGL Closed forest, evergreen broad leaf	CGL Closed forest, deciduous broad leaf	CGL Closed forest, mixed	CGL Closed forest, not matching any of the other definitions	CGL Open forest, evergreen broad leaf	CGL Open forest, deciduous broad leaf	CGL Open forest, mixed	CGL Open forest, not matching any of the other definitions	CGL Shrubs	CGL Oceans, seas	CGL Herbaceous vegetation	CGL Cultivated and managed vegetation / agriculture	CGL Urban / built up	CGL Bare / sparse vegetation	CGL Permanent water bodies	CGL Herbaceous wetland	GSL Peak/ridge (warm)	GSL Peak/ridge	GSL Mountain/divide	GSL Cliff	GSL Upper slope (warm)	GSL Upper slope	GSL Upper slope (flat)	GSL Lower slope (warm)	GSL Lower slope	GSL Lower slope (flat)	GSL Valley	GSL Valley (narrow)	GHH coefficient_of_variation_1km	GHH contrast_1km	GHH correlation_1km	GHH dissimilarity_1km	GHH entropy_1km	GHH homogeneity_1km	GHH maximum_1km	GHH mean_1km	GHH pielou_1km	GHH range_1km	GHH shannon_1km	GHH simpson_1km	GHH standard_deviation_1km	GHH uniformity_1km	GHH variance_1km	WCL bio01 Annual mean temperature	WCL bio02 Mean diurnal range mean of monthly max temp - min temp	WCL bio03 Isothermality bio02 div/bio07	WCL bio04 Temperature seasonality Standard deviation times 100	WCL bio05 Max temperature of warmest month	WCL bio06 Min temperature of coldest month	WCL bio07 Temperature annual range bio05-bio06	WCL bio08 Mean temperature of wettest quarter	WCL bio09 Mean temperature of driest quarter	WCL bio10 Mean temperature of warmest quarter	WCL bio11 Mean temperature of coldest quarter	WCL bio12 Annual precipitation	WCL bio13 Precipitation of wettest month	WCL bio14 Precipitation of driest month	WCL bio15 Precipitation seasonality	WCL bio16 Precipitation of wettest quarter	WCL bio17 Precipitation of driest quarter	WCL bio18 Precipitation of warmest quarter	WCL bio19 Precipitation of coldest quarter	CH-BIO bio01 mean annual air temperature	CH-BIO bio02 mean diurnal air temperature range	CH-BIO bio03 isothermality	CH-BIO bio04 temperature seasonality	CH-BIO bio05 mean daily maximum air temperature of the warmest month	CH-BIO bio06 mean daily minimum air temperature of the coldest month	CH-BIO bio07 annual range of air temperature	CH-BIO bio08 mean daily mean air temperatures of the wettest quarter	CH-BIO bio09 mean daily mean air temperatures of the driest quarter	CH-BIO bio10 mean daily mean air temperatures of the warmest quarter	CH-BIO bio11 mean daily mean air temperatures of the coldest quarter	CH-BIO bio12 annual precipitation amount	CH-BIO bio13 precipitation amount of the wettest month	CH-BIO bio14 precipitation amount of the driest month	CH-BIO bio15 precipitation seasonality	CH-BIO bio16 mean monthly precipitation amount of the wettest quarter	CH-BIO bio17 mean monthly precipitation amount of the driest quarter	CH-BIO bio18 mean monthly precipitation amount of the warmest quarter	CH-BIO bio19 mean monthly precipitation amount of the coldest quarter	G90 Compound Topographic Index	G90 Roughness	G90 Slope	G90 Stream Power Index	G90 Terrain Ruggedness Index	G90 Topographic Position Index	G90 Vector Ruggedness Measure	G90-GEOM flat	G90-GEOM pit	G90-GEOM peak	G90-GEOM ridge	G90-GEOM shoulder	G90-GEOM spur	G90-GEOM slope	G90-GEOM hollow	G90-GEOM footslope	G90-GEOM valley	CGIAR-ELE mean	GFC-PTC YEAR 2000 mean	GFC-LOSS year 2001	GFC-LOSS year 2002	GFC-LOSS year 2003	GFC-LOSS year 2004	GFC-LOSS year 2005	GFC-LOSS year 2006	GFC-LOSS year 2007	GFC-LOSS year 2008	GFC-LOSS year 2009	GFC-LOSS year 2010	GFC-LOSS year 2011	GFC-LOSS year 2012	GFC-LOSS year 2013	GFC-LOSS year 2014	GFC-LOSS year 2015	GFC-LOSS year 2016	GFC-LOSS year 2017	GFC-LOSS year 2018	GFC-LOSS year 2019	GFC-LOSS year 2020	GFC-LOSS year 2021	OSM-DIST mean	GP-CONSUNadj YEAR 2020 sum	YINSOLTIME mean	WBW-DIST mean	TSEASON-IZZO mean	PSEASON-IZZO mean
854c8997fffffff	53.23	0.18	16.10	0.05	5.32	0.30	24.41	0.04	0.37	0.00	17.58	0.02	1.14	2.25	3.55	0.03	0.00	37.24	0.49	23.26	5.93	0.43	6.28	0.00	0.86	0.92	4.37	0.01	0.38	0.00	36.12	0.94	0.11	43.69	0.36	5.50	8.22	0.30	553.95	143925.25	2701.17	26419.93	26458.88	3489.95	1346.63	6036.60	9324.83	1049.77	19304.32	8271.82	303.18	838.17	110570.64	248.92	107.04	71.28	1561.73	324.21	175.06	149.15	235.65	265.67	266.33	228.04	1635.50	261.50	59.88	45.22	672.56	209.71	224.95	549.74	2986.85	53.05	577.91	1344.62	3035.61	2944.04	91.57	2977.38	3002.49	3003.20	2969.91	16433.66	2143.31	784.98	308.38	5584.38	2511.82	2665.52	4075.12	-0.93	34.77	7.31	0.02	10.78	0.01	0.00	6.19	0.17	0.89	8.65	1.47	15.72	40.81	14.40	3.65	8.05	138.92	40.91	4.43	1.57	3.73	7.96	3.08	6.53	5.56	5.65	3.45	1.47	3.39	3.61	3.15	2.26	4.79	3.26	7.86	15.52	7.57	1.86	3.30	485.88	96091.08	4245.86	2846.30	1.55	38.49
854cd29bfffffff	92.63	0.45	6.86	0.00	0.02	0.04	0.00	0.00	0.00	0.00	54.67	0.08	6.62	0.85	16.43	0.09	0.00	18.53	2.57	0.00	0.14	0.00	0.00	0.00	0.00	0.00	2.27	0.00	0.38	0.02	51.65	1.96	0.00	35.54	2.01	0.00	5.42	0.75	333.72	64645.54	2366.46	18775.62	25792.33	4134.45	1488.12	6543.47	9171.98	762.60	17766.02	7992.01	214.45	893.31	48503.36	208.32	115.79	76.73	1051.30	277.10	127.15	149.95	217.98	193.39	219.94	192.83	1525.44	222.91	41.76	57.50	630.51	132.43	543.47	132.64	2946.50	55.31	630.91	1073.32	2989.30	2902.07	87.23	2957.44	2932.62	2959.15	2931.93	13602.64	1774.55	388.99	452.45	5072.78	1453.68	4211.96	1519.43	-1.83	74.70	15.28	0.08	23.21	0.09	0.01	0.00	0.52	0.91	10.58	0.00	22.12	37.61	16.21	0.01	12.04	830.58	72.25	1.58	2.31	1.83	4.40	4.99	3.43	4.12	6.39	3.59	3.88	4.80	7.41	1.02	2.29	2.56	8.00	17.94	2.10	2.97	9.85	4.54	1556.91	712.78	4060.95	8506.75	1.13	40.91
854cd477fffffff	77.78	0.01	18.68	1.13	2.25	0.14	0.02	0.00	0.00	0.00	44.34	0.00	0.00	1.13	13.80	0.00	0.00	29.68	0.70	0.00	6.91	0.49	2.95	0.00	0.00	0.00	3.76	0.10	0.40	0.00	30.91	3.10	0.00	41.16	2.39	7.69	9.53	0.96	383.77	83758.41	2532.01	21590.98	26627.64	3735.58	1350.42	6585.62	9237.43	870.54	18627.66	8168.88	245.23	808.25	63147.39	239.03	115.11	77.41	1111.19	310.66	162.85	147.81	248.76	224.22	251.10	222.48	1823.11	236.70	69.68	38.64	632.94	226.64	615.08	231.34	2961.58	82.72	704.70	1163.56	3020.04	2902.71	117.33	2971.31	2946.57	2974.38	2945.15	19176.43	2689.32	824.41	350.68	6320.46	2705.36	5754.26	2906.80	-1.03	52.65	10.70	0.12	16.50	-0.04	0.01	8.22	0.43	1.10	11.46	1.11	16.90	29.92	15.34	3.26	12.26	376.45	64.18	2.74	1.38	2.22	6.12	4.72	1.73	6.19	5.73	2.41	4.34	1.75	3.79	2.96	4.05	2.61	6.92	12.42	7.08	3.96	9.25	7.65	1009.65	60173.22	4025.37	7733.79	0.86	31.83
854cd46bfffffff	77.23	0.29	19.60	0.65	1.81	0.24	0.18	0.00	0.00	0.00	37.40	2.84	1.28	2.29	9.61	2.38	0.00	38.21	1.87	0.00	1.90	0.30	1.86	0.00	0.00	0.07	4.49	0.15	0.50	0.02	35.65	4.25	0.00	42.19	2.68	0.00	9.36	0.72	503.92	115267.73	2432.08	25185.14	27205.35	3363.36	1216.71	5679.26	9331.28	992.86	19612.54	8350.73	280.99	744.00	82401.52	211.04	126.76	76.92	1175.95	288.81	125.13	163.68	220.32	195.64	223.69	193.40	1239.19	184.13	38.22	44.82	443.71	131.83	395.16	140.73	2946.38	66.06	657.85	1141.68	2995.19	2894.84	100.34	2957.45	2931.59	2959.83	2931.25	17564.83	2585.98	612.50	417.95	6125.56	2240.78	5175.60	2263.67	-1.68	71.38	14.53	0.15	22.26	0.00	0.01	0.26	0.36	1.25	11.74	0.25	19.04	35.44	18.13	0.78	12.74	788.54	56.75	1.94	2.16	2.73	3.45	5.41	1.62	4.22	5.25	2.21	5.53	1.62	2.82	0.93	3.40	7.86	5.77	12.32	3.67	6.98	13.05	7.07	578.49	35183.51	3909.33	8356.37	1.13	46.13
854c8927fffffff	74.81	0.02	22.32	0.08	2.74	0.03	0.00	0.00	0.00	0.00	29.53	0.00	7.93	0.95	17.57	0.00	0.00	36.22	0.83	0.00	3.51	0.01	3.45	0.00	0.00	0.00	4.08	0.01	0.25	0.00	36.24	2.80	0.16	44.31	1.68	0.85	8.12	1.49	402.41	67267.73	2402.16	18776.99	25700.24	4160.32	1523.78	5204.59	9182.46	735.31	17721.63	7996.16	207.06	917.70	46544.69	210.75	121.87	72.86	1410.95	290.88	124.81	166.07	213.43	193.00	226.02	190.06	1243.61	192.09	54.56	37.20	410.75	187.80	277.83	214.89	2942.63	80.55	670.50	1338.61	3004.41	2884.30	120.12	2947.38	2925.93	2958.01	2924.34	19064.44	2675.46	896.09	295.45	5935.84	3180.46	4675.22	3388.92	-1.20	50.99	10.38	0.41	15.83	-0.01	0.00	0.49	0.59	0.93	12.19	0.84	18.39	35.48	17.06	0.94	13.08	815.36	59.09	8.51	2.77	1.33	14.69	3.92	3.75	4.31	5.85	2.30	2.50	4.07	4.45	0.71	2.29	2.04	6.47	18.15	5.18	3.32	1.37	2.04	545.51	32956.39	4085.09	10959.36	1.55	30.36
854c89b3fffffff	83.01	0.00	16.78	0.00	0.21	0.00	0.00	0.00	0.00	0.00	31.13	0.00	0.52	0.43	15.86	0.00	0.00	43.14	0.48	0.00	8.32	0.00	0.12	0.00	0.00	0.00	4.71	0.24	0.13	0.00	39.50	3.07	0.00	39.43	1.78	0.00	9.42	1.73	291.28	55924.91	2103.37	17878.66	25694.82	4184.82	1529.76	6668.80	9152.54	694.20	17502.41	7964.32	194.27	912.89	38155.07	229.85	108.38	71.41	1534.16	305.46	154.64	150.82	225.61	246.00	246.81	208.40	1585.33	212.74	72.45	33.42	538.60	258.70	275.68	383.55	2963.77	75.66	638.67	1461.83	3028.04	2909.78	118.25	2959.86	2981.29	2981.34	2945.10	18302.76	2290.24	977.74	281.60	5877.87	3145.78	3171.70	4467.35	-1.58	62.53	12.86	0.11	19.40	-0.03	0.01	0.00	0.78	1.07	11.02	0.00	19.44	37.28	18.50	0.06	11.84	520.39	68.52	3.80	5.02	5.91	6.36	4.17	5.86	5.70	4.50	3.96	3.15	2.03	6.33	0.38	2.39	1.80	6.31	11.62	5.18	4.56	7.17	3.80	595.41	5324.17	4004.35	12132.77	1.55	31.56
854cd4cbfffffff	85.32	0.02	13.50	0.09	0.94	0.08	0.03	0.02	0.00	0.00	54.44	0.00	1.71	1.27	12.71	0.00	0.00	24.61	0.98	0.00	3.33	0.01	0.92	0.00	0.01	0.02	2.95	0.00	0.00	0.00	43.43	2.88	0.13	36.46	2.80	0.94	9.05	1.36	441.41	87198.52	2466.84	21029.74	26183.20	3908.16	1402.52	5626.26	9233.07	838.51	18396.72	8114.10	240.33	845.03	67803.58	229.87	114.24	71.31	1467.61	308.02	148.97	159.05	228.11	212.91	246.17	208.54	1364.26	193.33	68.67	31.67	434.43	233.56	297.05	265.15	2957.02	86.48	674.79	1414.25	3023.81	2895.73	128.08	2962.03	2939.28	2973.28	2937.66	18240.46	2514.80	928.20	272.99	5515.53	3292.82	4208.68	3623.95	-1.23	50.28	10.26	0.17	15.63	-0.02	0.01	1.70	0.62	1.04	12.26	0.78	19.35	32.67	16.77	1.51	13.29	525.28	73.49	3.67	5.15	3.44	9.23	3.67	5.40	2.86	6.43	2.17	2.46	5.51	4.30	0.77	1.78	3.54	8.17	10.34	6.17	8.78	3.77	2.39	587.22	14227.34	4096.62	5258.06	1.47	27.17
854cd40bfffffff	69.87	0.21	23.01	3.42	1.77	1.67	0.03	0.01	0.00	0.00	38.21	0.00	0.10	0.91	10.21	0.00	0.00	34.36	2.77	0.00	8.14	2.80	1.87	0.00	0.00	0.64	5.37	0.15	0.11	0.00	29.26	1.41	0.00	44.78	0.54	8.23	9.56	0.60	560.74	110522.62	2895.09	23547.07	26743.45	3668.93	1330.09	5553.40	9309.39	973.48	19108.92	8245.45	279.64	805.42	91464.58	247.62	114.72	72.63	1329.10	326.07	169.21	156.85	256.49	230.91	262.52	228.29	1850.06	248.65	86.72	30.62	598.80	272.52	552.16	291.37	2967.43	80.62	683.72	1283.67	3026.60	2908.76	117.85	2978.24	2951.16	2981.30	2949.33	19828.18	2856.40	903.69	304.29	6231.05	3141.72	5228.12	3445.79	-0.96	46.64	9.55	0.08	14.45	-0.01	0.00	8.96	0.35	0.94	10.74	1.39	16.59	32.63	14.03	3.20	11.17	282.83	63.47	4.38	1.19	1.89	2.77	2.83	1.06	3.66	4.70	1.11	1.69	1.40	2.38	0.85	3.22	4.02	13.86	18.63	13.11	8.21	5.99	3.05	698.01	41697.46	4029.71	4516.80	1.19	27.95
854cd423fffffff	51.80	0.03	39.96	0.25	7.44	0.49	0.01	0.02	0.00	0.00	9.56	0.00	0.00	1.23	4.98	0.00	0.00	56.12	1.27	0.00	18.53	0.95	7.36	0.00	0.00	0.00	1.85	0.01	0.00	0.00	38.48	0.02	5.28	31.81	0.00	8.30	11.71	2.54	534.35	137891.16	2910.79	27054.18	27408.18	3237.46	1218.22	6260.92	9333.29	1114.72	20030.66	8392.54	320.88	733.78	114024.98	257.58	105.57	76.97	1105.24	322.79	186.46	136.32	268.03	242.87	269.39	241.27	1948.79	259.33	69.18	45.63	714.45	212.59	709.86	231.38	2982.45	79.57	709.84	1064.63	3038.59	2926.58	112.00	2992.22	2968.89	2994.53	2967.57	17506.42	2287.29	698.76	390.81	6044.30	2204.35	5802.36	2379.64	-0.03	13.48	2.73	0.05	4.26	0.00	0.00	10.67	0.30	0.50	10.47	8.63	11.79	28.88	9.89	7.52	11.34	57.84	37.99	2.33	1.41	2.47	4.72	5.49	2.10	3.65	3.54	2.50	7.04	1.27	4.53	4.15	7.54	5.34	3.01	9.13	6.24	8.53	11.06	3.94	157.90	213749.80	4363.78	4739.16	1.12	39.87
854cc6c7fffffff	15.63	0.00	18.05	0.01	1.04	0.62	60.88	2.23	1.55	0.00	1.59	0.00	0.00	1.79	0.15	0.00	0.00	6.90	1.49	63.16	22.10	0.50	0.72	0.00	0.05	1.55	0.00	0.00	0.00	0.00	4.04	0.00	38.97	1.30	0.00	40.69	14.04	0.96	540.74	209157.89	1689.68	30889.32	26849.86	3169.99	1252.36	6229.03	9392.44	1190.65	19656.62	8365.06	332.39	774.76	132146.05	265.27	80.88	70.83	1222.40	321.36	207.80	113.56	271.63	249.28	279.27	248.42	1256.04	164.29	55.20	32.33	409.30	184.12	334.91	211.74	2991.50	35.43	545.39	1070.91	3023.43	2958.75	64.68	2998.24	2977.30	3004.49	2977.25	13326.10	1691.42	590.20	284.89	4265.93	2143.85	3575.35	2143.93	1.06	2.61	0.50	0.04	0.85	0.04	0.00	85.00	0.00	0.16	1.69	4.72	0.73	2.90	0.16	4.24	0.41	8.15	10.67	0.66	3.86	0.72	1.43	7.83	3.76	6.88	6.22	1.39	0.75	1.21	1.86	1.98	2.21	8.35	6.19	8.15	6.22	22.13	5.07	3.14	283.11	7678.01	4464.99	810.47	1.20	26.09
854c892ffffffff	93.43	0.21	6.05	0.13	0.17	0.01	0.00	0.00	0.00	0.00	43.30	0.00	38.47	0.56	8.70	0.00	0.02	7.46	0.31	0.00	1.12	0.00	0.06	0.00	0.00	0.00	3.40	0.03	0.41	0.00	40.70	3.00	0.00	41.07	1.77	0.00	8.56	1.06	318.31	37540.97	2357.54	14477.84	24403.43	4784.52	1749.18	5106.35	9077.22	572.22	16222.62	7671.75	162.38	1059.15	27417.78	175.22	126.81	75.15	1324.96	254.71	86.93	167.78	180.58	158.00	189.29	155.85	1255.51	219.15	44.14	46.18	450.15	145.34	339.22	172.06	2914.02	77.55	683.53	1207.06	2970.84	2857.46	113.38	2919.66	2898.39	2927.58	2897.38	20609.05	2992.50	798.26	360.02	6748.59	2912.21	5311.03	2952.27	-1.68	71.21	14.52	0.24	22.23	-0.02	0.01	0.00	0.48	0.94	12.93	0.01	20.14	31.57	19.70	0.03	14.20	1332.89	83.22	0.82	2.67	0.97	15.85	2.69	1.13	3.11	4.00	2.47	3.88	3.33	7.46	0.60	2.14	1.34	7.88	21.37	3.62	3.14	2.24	9.30	1488.99	3583.55	3929.96	21489.70	1.61	40.63
854cd63bfffffff	60.04	37.59	2.17	0.01	0.01	0.18	0.00	0.00	0.00	0.00	29.90	0.29	2.77	1.15	7.34	0.19	0.00	42.22	16.04	0.00	0.05	0.05	0.00	0.00	0.00	0.00	3.29	0.00	0.00	0.00	43.85	2.29	0.00	43.17	1.61	0.00	5.55	0.24	376.08	47927.90	2125.92	16132.45	24728.36	4562.69	1712.02	4809.08	9123.29	620.60	16640.00	7760.36	178.44	1028.12	33520.08	232.08	115.81	74.94	1113.94	302.51	149.16	153.35	241.88	217.00	244.44	215.43	1044.71	160.04	21.75	60.11	432.76	76.52	365.76	77.62	2964.60	76.53	682.35	1152.82	3019.73	2907.76	111.97	2975.64	2949.01	2978.11	2948.99	9674.53	1431.51	208.92	492.42	3800.28	874.48	3006.51	907.54	-1.33	53.63	11.06	0.05	16.77	0.07	0.00	0.02	0.19	1.21	12.12	0.14	19.14	36.57	16.98	0.34	13.28	553.24	39.07	1.82	1.58	0.28	0.37	0.57	0.47	0.98	0.24	0.37	0.74	0.40	0.67	1.54	2.60	15.82	7.50	0.17	0.22	3.40	60.15	0.12	4302.20	674.26	4131.12	6651.39	1.16	50.51
854cd457fffffff	80.56	0.08	10.17	2.08	6.50	0.53	0.07	0.02	0.00	0.00	39.05	0.00	0.73	0.48	12.05	0.00	0.00	32.29	2.42	0.00	4.42	1.40	7.09	0.00	0.00	0.08	2.74	0.17	0.65	0.03	22.97	5.66	2.39	28.87	4.29	25.41	5.95	0.86	561.47	135024.51	2809.71	25878.74	26996.68	3498.05	1272.08	5899.55	9325.63	1060.65	19402.96	8298.58	303.69	772.89	103484.90	232.37	118.97	72.04	1403.31	313.83	149.78	164.05	238.02	214.78	248.07	212.08	1618.93	237.04	75.34	31.90	517.61	243.68	439.07	271.71	2957.00	77.90	673.30	1319.49	3014.79	2899.14	115.65	2964.17	2940.21	2971.34	2938.60	21630.22	3068.36	1030.85	284.71	6533.20	3635.38	5361.14	4055.14	-0.74	58.91	11.80	0.23	18.34	0.05	0.01	26.16	0.37	0.58	7.83	2.52	13.57	25.27	11.79	4.28	7.63	503.13	59.95	3.21	3.41	1.93	4.52	3.50	4.92	7.85	5.74	2.25	2.89	5.06	7.51	0.72	1.89	1.22	6.80	17.81	5.98	3.31	4.98	4.47	1520.22	88440.45	3987.01	5278.27	1.28	28.65
856725b7fffffff	23.13	44.74	16.62	2.11	1.34	4.47	7.09	0.49	0.00	0.00	6.03	0.00	0.00	0.15	4.68	0.00	0.00	26.63	45.64	0.00	2.02	0.77	1.00	0.16	8.97	3.96	2.30	0.00	0.00	0.00	30.36	2.60	0.97	30.13	2.03	22.97	7.83	0.79	672.00	132857.49	2484.22	23335.28	25451.65	4067.05	1559.68	4193.78	9171.67	978.56	17946.09	7959.29	279.40	963.27	105246.42	257.10	120.84	76.57	1140.31	332.14	175.38	156.77	267.41	240.13	269.97	240.13	967.83	151.16	23.23	55.81	390.27	79.47	312.03	79.47	2988.65	93.88	726.97	962.19	3054.47	2925.42	129.05	2997.87	2975.20	3001.08	2975.20	7612.88	1208.37	151.13	526.80	2912.24	607.36	2388.75	607.36	-0.36	35.58	7.53	0.35	11.19	-0.03	0.00	25.15	0.31	0.60	5.68	1.34	13.09	34.66	8.65	4.31	6.22	256.22	16.73	1.00	1.01	1.23	6.20	1.67	7.03	5.59	1.56	2.95	1.57	1.05	2.71	14.36	3.88	15.61	4.19	2.05	0.59	2.78	20.58	2.39	1035.18	2689.93	4229.26	3835.65	1.30	59.65
854cf26ffffffff	79.36	0.01	13.07	0.36	7.18	0.02	0.00	0.00	0.00	0.00	33.81	0.00	0.00	2.78	11.76	0.00	0.00	32.19	2.57	0.00	7.12	2.18	7.58	0.00	0.00	0.00	2.13	0.00	0.00	0.00	28.38	0.00	5.42	30.75	0.01	24.79	7.53	0.98	491.31	138800.00	2395.59	26265.62	27073.94	3410.50	1283.56	6772.27	9324.21	1046.58	19480.55	8313.14	299.95	767.94	112477.47	248.37	98.82	70.97	1464.21	316.16	177.86	138.30	251.36	229.72	263.96	227.13	1477.62	191.22	80.19	26.92	454.12	252.36	370.36	312.03	2972.82	91.73	696.91	1387.99	3041.57	2910.08	131.48	2968.63	2955.61	2988.43	2953.80	17181.85	2069.41	908.47	231.71	5134.20	3218.14	3974.61	3731.69	-0.13	20.28	4.14	0.03	6.45	-0.01	0.00	21.84	0.37	0.85	8.25	8.21	9.69	24.10	8.86	8.77	9.05	227.00	60.77	5.54	4.21	5.42	6.34	5.55	3.31	3.22	7.17	3.53	6.35	3.79	4.25	2.32	5.01	1.96	3.63	7.61	7.52	7.44	2.62	3.22	210.37	131202.80	4306.78	10685.71	1.42	25.88
85672537fffffff	51.64	25.44	15.77	0.55	1.96	0.25	2.63	0.76	1.00	0.00	5.15	0.41	0.00	2.72	1.52	0.18	0.00	27.27	47.16	1.65	5.65	2.53	2.49	0.00	0.90	2.37	0.35	0.00	0.00	0.00	38.00	0.00	5.13	38.26	0.00	13.55	4.14	0.57	433.22	69634.86	2537.16	18132.73	24923.86	4463.83	1653.40	5169.54	9119.49	728.05	17120.92	7838.47	208.18	1003.03	50893.18	255.13	116.94	74.57	1244.18	331.27	175.49	155.79	267.26	238.32	270.18	238.02	848.74	137.66	20.97	55.77	346.11	79.82	262.87	79.85	2991.04	64.40	632.31	1136.94	3044.57	2942.90	101.67	3001.00	2976.85	3005.69	2976.73	6844.63	1023.78	198.39	478.31	2753.12	745.42	1613.78	780.44	0.13	17.70	3.76	0.19	5.54	0.00	0.00	15.93	0.25	0.34	5.15	3.64	12.31	42.00	9.98	4.33	6.05	214.95	38.55	5.22	1.53	2.49	7.18	4.84	5.34	5.33	14.57	5.65	2.62	4.03	25.62	2.75	4.41	1.33	1.57	0.81	1.03	0.57	1.40	1.70	724.68	17038.73	4360.18	5993.55	1.59	55.98
854c89abfffffff	46.54	4.68	34.13	5.42	8.44	0.77	0.02	0.00	0.00	0.00	3.68	0.17	0.03	0.47	2.80	0.51	0.00	49.34	15.69	0.00	10.96	6.79	9.52	0.00	0.00	0.02	2.26	0.01	0.00	0.00	35.19	0.28	4.50	36.74	0.11	12.51	7.23	1.18	578.90	144857.88	2185.36	26338.64	26986.77	3445.95	1270.71	5416.89	9295.70	1028.09	19265.49	8276.35	294.04	764.46	109483.08	252.57	110.95	70.55	1580.80	331.11	174.89	156.22	258.05	233.05	270.60	230.08	1072.98	175.34	50.66	38.34	360.80	174.32	223.72	195.34	2980.56	96.49	672.59	1534.95	3059.41	2916.01	143.41	2985.01	2963.39	2999.78	2960.76	13883.66	2084.98	667.89	343.44	4650.98	2463.49	2747.38	2490.93	-0.20	20.71	4.20	0.42	6.57	0.00	0.00	10.91	0.45	1.11	10.26	5.10	12.20	31.77	10.81	7.10	10.29	255.92	32.94	0.93	4.36	1.64	5.76	5.38	8.89	5.34	9.04	6.19	4.69	3.81	3.43	4.86	6.94	4.69	2.53	2.80	8.95	1.41	3.56	4.82	269.30	132204.06	4294.46	5450.27	1.69	39.94
854c89a3fffffff	50.18	0.15	14.80	2.05	32.19	0.62	0.01	0.00	0.00	0.00	4.10	0.00	0.00	0.27	3.21	0.00	0.00	23.75	6.74	0.00	13.80	7.14	40.98	0.00	0.00	0.00	1.08	0.00	0.00	0.00	20.65	0.02	7.94	23.95	0.01	38.55	6.76	1.03	995.09	271964.80	2691.85	37070.60	28148.87	2695.20	1068.83	4869.65	9457.27	1475.94	21317.17	8620.46	428.88	660.27	218096.93	248.95	106.24	70.66	1557.11	323.76	174.44	149.32	249.85	238.14	266.49	226.93	1175.77	171.48	59.06	33.47	374.12	199.13	246.30	241.02	2978.74	91.78	672.51	1493.46	3053.56	2917.15	136.40	2978.94	2993.56	2996.81	2959.17	13129.16	1728.81	712.29	291.04	4064.32	2413.87	2416.63	2796.02	0.25	17.80	3.66	0.33	5.56	-0.02	0.00	33.31	0.20	0.36	5.01	7.70	7.70	25.27	6.30	8.78	5.37	258.33	19.32	5.38	3.26	0.81	5.96	1.72	4.94	3.43	4.69	5.22	3.69	3.40	3.96	3.46	7.03	3.54	5.84	6.61	3.72	4.89	7.46	10.97	84.51	568574.42	4320.64	8031.44	1.53	32.35
854cd647fffffff	67.99	10.22	16.95	1.26	2.60	0.94	0.00	0.05	0.00	0.00	17.29	0.55	0.50	2.76	9.28	0.29	0.00	28.90	32.88	0.00	3.94	0.27	3.20	0.00	0.00	0.14	1.31	0.00	0.37	0.05	33.10	1.80	1.77	44.06	1.79	9.84	5.18	0.74	551.11	126086.00	2723.63	24514.91	26706.84	3608.89	1339.92	5615.83	9329.02	998.91	19319.93	8306.28	286.81	800.93	96242.31	245.41	124.24	76.00	1167.06	321.43	158.96	162.48	255.30	227.70	258.05	227.69	997.93	158.18	16.93	57.73	408.23	67.96	332.56	67.96	2973.78	97.06	717.47	1052.26	3041.46	2906.24	135.21	2984.19	2959.45	2987.08	2959.45	8418.29	1381.34	182.31	522.28	3078.43	659.39	2469.82	659.39	-1.00	59.97	12.17	0.13	18.46	-0.10	0.01	8.54	0.21	0.59	6.97	0.73	15.49	44.03	13.94	1.61	7.91	422.37	36.77	1.77	1.15	1.06	2.41	1.54	1.71	4.20	4.16	5.23	3.11	1.92	4.93	7.16	9.63	3.00	10.27	8.76	8.33	7.65	8.36	3.65	1050.62	43786.67	4053.04	10759.05	1.31	58.42
854cd453fffffff	87.57	0.02	11.22	0.73	0.43	0.04	0.00	0.00	0.00	0.00	54.35	0.00	8.13	1.03	15.12	0.02	0.01	18.95	0.50	0.00	1.78	0.00	0.11	0.00	0.00	0.00	5.70	0.03	0.19	0.00	42.52	0.97	0.00	37.96	0.73	0.00	9.61	2.29	356.71	48389.65	2757.79	16221.11	25107.11	4515.96	1624.42	5303.86	9157.08	656.69	17063.14	7859.20	186.77	976.80	36567.58	185.44	127.98	75.45	1332.96	265.65	97.09	168.56	190.95	168.23	199.49	165.85	1200.39	193.08	41.70	40.43	406.74	147.25	320.66	180.63	2920.13	77.75	668.88	1343.90	2978.28	2862.09	116.19	2925.64	2903.04	2934.95	2901.72	21337.68	2937.44	1050.42	274.30	6398.64	3663.61	5104.77	4082.87	-1.38	52.80	10.58	0.22	16.64	0.03	0.01	0.00	0.86	1.84	16.31	0.00	18.29	27.01	18.16	0.13	17.41	1145.43	70.44	2.84	2.52	1.70	2.90	5.46	1.56	2.73	1.51	3.88	1.59	2.87	11.17	1.12	3.41	2.74	4.41	17.95	8.54	4.09	12.39	4.61	1350.90	8819.50	4073.20	8063.92	1.47	25.71
854cf373fffffff	69.55	0.00	21.94	2.55	1.52	0.01	2.34	0.32	1.78	0.00	12.05	0.08	0.00	2.04	8.19	0.00	0.00	54.31	3.29	1.85	12.83	0.84	2.01	0.00	0.37	2.14	3.15	0.00	0.00	0.00	29.74	0.00	4.05	34.31	0.00	17.12	10.18	1.47	394.05	99067.52	2689.33	23202.54	26896.72	3560.69	1242.53	6991.05	9322.68	946.14	19169.55	8288.30	266.72	766.66	74693.92	254.54	98.19	75.13	1156.47	316.44	186.63	129.81	262.89	239.32	267.12	238.35	2007.48	224.28	92.72	26.33	615.35	303.65	574.13	351.62	2984.00	68.24	663.68	1187.50	3037.33	2934.59	102.74	2992.00	2969.00	2997.90	2968.27	23900.64	3015.80	958.13	310.94	7653.73	3508.14	6545.98	3982.16	0.08	17.83	3.54	0.12	5.70	-0.01	0.00	22.64	1.07	1.57	9.77	4.66	10.53	23.99	9.00	6.41	10.36	76.44	62.12	1.65	3.40	3.68	6.37	4.15	1.77	3.94	6.72	2.36	3.23	1.51	4.65	9.42	4.19	3.87	12.88	9.72	5.15	3.07	4.08	4.19	639.94	18727.83	4331.60	5267.75	1.24	28.31
854c89c7fffffff	27.48	30.03	32.40	5.64	2.27	2.13	0.05	0.00	0.00	0.00	0.87	0.00	0.00	0.16	1.38	0.00	0.00	34.28	30.86	0.00	17.02	13.14	2.24	0.00	0.02	0.02	0.87	0.00	0.00	0.00	23.14	0.00	7.84	32.53	0.00	28.78	6.58	0.26	521.56	101967.29	2256.30	23814.35	26863.08	3527.30	1305.55	5174.19	9245.82	941.25	19001.64	8213.27	266.44	793.81	73213.06	261.84	119.08	71.93	1584.42	344.54	179.80	164.73	263.89	250.01	279.92	239.61	949.04	127.07	39.43	33.73	315.98	159.84	176.04	191.67	2993.14	94.22	646.12	1616.41	3073.63	2927.91	145.72	2998.00	2973.56	3013.76	2973.10	10517.67	1583.54	478.99	367.94	3484.81	1758.00	2273.25	1776.24	0.47	13.82	2.84	0.27	4.35	-0.02	0.00	28.74	0.05	0.57	6.50	6.00	9.33	25.88	7.82	8.41	6.70	139.72	22.06	2.10	2.11	2.88	5.78	7.44	11.66	10.03	12.68	5.97	8.31	3.36	2.86	2.65	2.32	2.78	3.23	2.07	4.34	1.43	2.79	3.24	479.89	23822.16	4347.20	3547.01	1.60	28.98
854c89c3fffffff	19.51	31.38	46.36	0.32	0.67	1.02	0.07	0.04	0.64	0.00	7.02	0.08	0.00	0.94	5.18	0.10	0.00	40.68	19.38	0.00	15.24	10.43	0.31	0.00	0.01	0.64	4.89	0.02	0.00	0.00	34.72	0.15	0.14	45.29	0.07	7.99	6.39	0.34	387.73	66141.21	2571.33	18791.44	25738.83	4151.89	1516.35	5607.47	9184.13	763.61	17833.33	8004.70	215.91	906.68	48838.20	258.47	117.33	72.08	1561.77	339.84	178.16	161.68	255.32	256.97	276.32	236.77	993.59	125.92	44.54	31.35	330.39	172.20	181.15	223.00	2993.36	85.49	628.70	1585.38	3068.12	2932.32	135.80	2999.12	2976.47	3013.44	2973.76	11082.21	1553.56	501.68	358.51	3816.41	1879.24	2246.72	1958.67	-0.62	24.84	5.14	0.02	7.75	0.00	0.00	4.79	0.14	1.36	10.27	2.38	14.42	37.35	14.13	5.47	9.69	173.60	38.85	2.54	3.39	1.57	8.33	2.84	9.92	6.85	9.39	4.01	7.48	8.07	7.13	4.67	2.46	1.38	1.92	1.93	4.79	8.72	0.77	1.82	695.37	3363.50	4275.52	4345.16	1.57	25.32
854cf31bfffffff	79.98	0.00	10.93	0.00	0.82	0.08	8.18	0.01	0.00	0.00	28.09	0.00	0.03	1.19	16.76	0.00	0.00	38.38	1.06	7.56	5.30	0.09	0.79	0.00	0.46	0.28	5.35	0.15	0.00	0.00	42.66	0.61	0.00	39.52	0.35	1.36	9.33	0.68	344.60	101323.37	2482.55	20575.03	25752.71	4076.96	1492.94	6922.60	9181.87	828.62	17764.85	7992.13	234.51	897.69	68670.99	250.36	95.40	74.06	1213.86	312.00	183.92	128.08	252.41	234.88	263.60	233.40	1960.93	242.75	92.73	27.59	614.73	310.18	497.68	381.73	2984.20	47.01	587.70	1177.51	3025.11	2945.48	79.63	2982.41	2972.72	2998.55	2969.05	24097.02	3087.55	1204.68	272.51	8043.36	3969.73	6026.68	4391.95	-1.09	39.16	7.88	0.06	12.42	0.10	0.00	2.04	1.26	2.19	14.13	0.72	17.75	30.18	14.82	2.14	14.76	174.25	72.17	1.68	1.98	2.68	6.55	2.58	3.05	3.39	5.48	2.89	3.83	4.67	3.82	2.23	3.14	6.74	11.37	13.54	4.47	7.91	5.11	2.89	503.48	22781.70	4233.63	2670.11	1.14	31.60
854cf243fffffff	45.45	0.05	8.38	0.01	2.79	0.20	42.31	0.81	0.00	0.00	17.18	0.00	1.09	0.88	2.90	0.00	0.00	23.10	0.83	41.16	6.50	0.69	2.96	0.00	0.82	1.89	1.63	0.00	0.00	0.00	41.23	0.00	0.52	37.15	0.00	11.79	7.45	0.22	499.09	168113.43	2269.79	26242.72	25749.02	3742.40	1454.27	6242.80	9233.31	1044.18	18371.31	8087.34	297.50	905.72	117171.97	249.35	94.84	70.00	1470.20	316.79	182.27	134.52	235.96	264.94	265.58	229.79	1778.02	266.67	81.12	37.07	667.53	263.88	275.17	543.48	2985.72	32.93	494.41	1198.34	3020.13	2953.91	66.22	2977.07	2998.94	2999.96	2970.32	16770.12	2416.74	780.97	363.38	6370.25	2415.40	2565.31	4446.61	-0.29	18.97	3.80	0.02	6.04	0.03	0.00	9.25	0.46	1.28	11.05	4.58	13.15	32.43	12.01	4.89	10.90	96.25	33.97	4.39	3.78	1.55	6.76	7.36	7.64	4.22	5.85	4.90	1.62	5.12	4.86	0.56	5.46	5.04	6.07	4.17	3.16	9.61	6.53	1.36	312.88	35672.39	4350.46	1613.53	1.52	41.10
854cd0c3fffffff	34.15	40.46	15.39	2.55	1.50	3.35	2.60	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.08	0.00	0.00	29.63	59.25	1.91	1.89	3.97	1.34	1.08	0.59	0.25	3.90	0.13	0.00	0.00	33.87	0.22	2.97	44.23	0.02	7.65	6.80	0.19	675.38	131950.16	2356.23	24870.36	26525.65	3657.53	1335.00	4287.66	9281.57	969.35	18777.28	8169.10	281.84	820.28	96837.04	258.68	103.45	75.91	1023.94	323.77	188.33	135.44	268.15	243.87	270.96	243.86	924.22	143.78	22.56	61.18	391.16	70.35	322.51	70.35	2989.69	39.80	568.56	1010.51	3023.56	2953.78	69.78	2999.84	2977.82	3002.26	2976.20	11160.36	1722.28	288.50	544.48	4606.56	1076.86	3662.48	1119.73	-0.56	30.18	6.18	0.14	9.44	0.04	0.00	12.43	0.13	1.26	10.16	2.38	14.44	31.29	12.11	5.48	10.33	165.84	7.61	0.69	0.94	1.33	0.21	0.86	1.41	3.98	0.17	8.01	1.16	0.13	4.58	15.58	7.90	20.91	6.34	5.14	1.54	6.55	11.83	0.73	1084.58	12573.17	4262.08	4483.32	1.00	60.10
854cd42ffffffff	27.13	0.01	7.35	0.03	55.28	0.63	9.42	0.15	0.00	0.00	3.74	0.00	0.00	0.28	1.08	0.00	0.00	14.90	0.80	7.39	5.38	0.22	63.85	0.00	1.30	1.07	0.02	0.00	0.00	0.00	37.80	0.00	15.78	23.79	0.00	9.25	10.99	2.36	1420.67	401081.36	3097.39	44948.08	28152.66	2448.15	1054.97	4137.26	9509.98	1857.84	21941.92	8689.72	544.77	665.02	330235.97	258.40	99.36	75.99	1066.49	319.87	189.96	129.91	266.35	244.05	270.20	243.21	1703.42	237.19	55.75	48.21	636.98	180.74	592.93	198.09	2985.17	71.79	703.32	1000.54	3035.58	2933.72	101.86	2994.81	2972.18	2996.77	2971.31	16194.21	2133.66	626.81	409.46	5710.63	1962.29	5475.20	2126.68	0.29	8.87	1.75	0.17	2.82	0.03	0.00	25.45	0.22	0.54	8.35	10.36	6.78	23.76	6.32	9.99	8.22	36.58	13.60	3.52	3.89	11.03	6.69	5.13	4.27	4.04	4.89	4.04	3.86	3.77	2.53	2.11	4.57	5.55	7.65	10.44	2.01	3.61	4.43	1.97	41.12	1597642.83	4414.15	1926.86	1.12	40.33
854cf347fffffff	35.28	0.00	0.96	0.00	0.00	0.01	63.23	0.09	0.42	0.00	33.16	0.00	0.00	0.27	1.23	0.00	0.00	1.67	0.00	61.76	0.06	0.00	0.00	0.00	1.00	0.85	5.20	0.00	0.00	0.00	47.92	0.00	0.09	33.27	0.00	0.63	9.31	3.59	377.12	133090.84	1943.31	21661.97	24609.55	4231.01	1658.47	7022.74	9084.34	881.88	16959.00	7795.27	253.77	1031.90	98257.96	252.89	99.65	75.26	1173.81	315.34	183.74	131.60	263.50	237.58	265.39	236.11	1956.32	212.83	96.55	24.64	589.86	304.64	569.57	354.29	2986.74	71.91	668.92	1217.09	3042.74	2935.34	107.40	2993.46	2971.40	3001.08	2970.31	21276.51	2590.88	897.90	281.92	6643.69	3288.24	5774.68	3756.48	-0.68	32.87	5.73	0.14	11.20	0.13	0.00	0.97	6.59	7.11	20.41	0.23	13.09	19.23	12.16	0.60	19.62	128.86	34.58	0.53	0.18	3.44	0.40	7.35	0.26	0.60	0.46	0.27	0.64	0.49	0.69	1.88	0.93	28.42	27.55	1.44	3.40	2.31	10.99	7.78	6081.08	0.00	4229.64	822.51	1.18	28.64
854cf303fffffff	30.33	0.00	2.50	0.01	0.57	0.02	66.43	0.15	0.00	0.00	12.49	0.00	0.05	0.40	7.60	0.00	0.00	11.38	0.16	65.75	0.47	0.00	0.64	0.00	0.37	0.69	5.09	0.04	0.00	0.00	49.53	0.26	0.04	39.79	0.09	0.43	4.51	0.24	460.97	187298.14	2220.10	26737.66	25643.44	3735.23	1442.32	6963.18	9194.60	1044.76	17982.37	8017.27	305.98	898.58	130291.68	249.24	96.03	73.88	1232.13	311.92	182.55	129.37	253.83	233.44	262.78	232.20	2017.33	244.57	96.99	26.81	630.80	320.27	523.10	390.07	2986.79	39.89	560.75	1111.56	3023.07	2952.26	70.82	2988.83	2975.38	3000.06	2972.22	21575.03	2692.96	1051.50	272.54	7119.90	3501.96	5519.02	3820.33	-1.36	38.62	7.88	0.02	12.24	0.17	0.00	0.34	0.98	2.23	13.44	0.38	18.78	34.71	15.90	1.13	12.13	175.13	27.27	1.17	0.86	3.03	5.40	2.62	3.06	2.49	7.60	1.76	2.46	1.75	4.65	3.22	2.97	5.56	12.54	20.44	4.79	3.55	5.54	4.55	715.56	18619.42	4270.50	642.08	1.39	28.65
854cd5b3fffffff	24.88	0.21	31.60	2.07	13.33	0.28	27.62	0.02	0.00	0.00	3.10	0.00	0.00	0.32	1.16	0.00	0.00	10.83	4.84	24.41	19.91	16.11	16.12	0.00	2.36	0.85	0.00	0.00	0.00	0.00	23.27	0.00	34.71	13.64	0.00	17.10	7.26	4.02	855.49	262727.28	2577.46	35469.26	27388.93	2919.56	1205.36	5502.42	9386.75	1462.35	20630.23	8483.18	425.98	735.09	220671.78	260.63	99.60	72.33	1248.78	326.45	189.86	136.60	272.61	243.92	274.64	243.25	1078.15	148.21	38.80	43.63	399.07	122.21	351.72	136.73	2995.13	65.24	665.44	1084.46	3045.55	2947.63	97.92	3005.05	2981.51	3008.62	2981.36	11544.17	1760.77	366.96	455.09	4136.39	1291.57	3391.05	1459.76	0.68	6.49	1.29	0.33	2.10	0.00	0.00	52.96	0.46	0.13	3.11	10.25	3.25	15.06	2.76	7.69	4.33	47.97	13.39	6.31	8.96	8.70	2.22	12.21	5.09	12.46	4.70	9.86	1.98	4.50	2.01	3.69	4.48	8.63	1.39	0.54	0.39	0.70	0.17	1.02	262.76	233356.75	4436.70	1760.89	1.36	47.33
854cc64ffffffff	27.97	0.58	61.97	8.20	1.10	0.08	0.10	0.00	0.00	0.00	3.27	0.00	0.00	0.42	1.61	0.00	0.00	17.79	6.48	0.00	43.43	26.30	0.67	0.00	0.00	0.02	0.02	0.00	0.00	0.00	12.71	0.00	43.49	5.63	0.00	24.63	10.43	3.09	456.16	108166.73	2529.45	24700.71	27077.20	3434.98	1253.56	6266.78	9284.90	985.71	19348.30	8302.26	282.59	756.37	82300.64	262.16	98.10	72.50	1257.42	327.68	193.30	134.38	274.23	245.36	276.04	244.27	1180.42	153.32	53.96	35.00	402.77	165.50	362.95	184.83	2992.32	64.64	664.17	1102.21	3042.01	2944.79	97.21	2997.23	2978.33	3006.09	2978.28	12314.26	1853.60	437.30	411.81	4211.60	1533.52	3546.96	1680.73	0.67	6.03	1.20	0.40	1.94	0.01	0.00	69.25	0.37	0.06	3.04	5.88	2.77	8.16	2.92	3.70	3.85	65.03	22.66	1.65	1.79	2.07	0.76	4.85	7.29	3.42	7.57	9.78	4.55	8.72	4.59	6.85	8.30	6.44	3.77	2.96	4.07	1.60	4.10	4.87	494.02	10767.10	4439.33	5854.38	1.44	41.01
854c893bfffffff	64.41	0.09	34.69	0.01	0.77	0.03	0.00	0.00	0.00	0.00	16.28	0.00	6.78	2.27	16.89	0.00	0.02	47.57	2.04	0.00	7.26	0.13	0.76	0.00	0.00	0.00	2.03	0.04	0.35	0.00	45.04	2.03	0.00	38.97	1.11	0.00	7.58	2.85	403.65	65273.56	1843.63	19009.77	26002.69	4044.23	1453.03	5038.05	9199.02	725.51	17742.02	8020.11	202.77	866.02	43205.85	224.09	119.47	71.43	1478.86	305.80	139.72	166.08	230.09	205.42	240.60	202.75	1270.64	217.26	55.28	41.65	454.45	189.99	267.28	205.68	2951.70	88.88	674.11	1387.32	3022.42	2890.60	131.82	2955.50	2933.46	2968.85	2933.46	17312.55	2796.79	667.00	413.00	6316.65	2358.18	4058.18	2358.18	-1.10	43.55	8.84	0.48	13.63	-0.02	0.00	0.00	0.77	1.23	13.71	0.10	19.04	32.04	17.64	0.13	15.35	709.30	50.88	5.78	2.03	2.91	9.50	5.11	3.43	6.26	5.08	3.56	1.21	6.66	9.15	0.45	1.28	0.60	5.05	7.31	8.21	12.21	1.52	2.67	596.49	11630.28	4134.58	15276.46	1.79	44.91
854cf267fffffff	92.92	0.02	6.97	0.03	0.05	0.00	0.00	0.00	0.00	0.00	63.54	0.00	0.00	2.26	11.46	0.00	0.00	20.96	0.28	0.00	1.45	0.00	0.05	0.00	0.00	0.00	4.00	0.00	0.13	0.00	43.17	0.17	0.00	42.81	0.12	0.00	8.55	1.06	345.98	79441.81	2115.36	21555.06	26700.89	3676.11	1314.04	6862.96	9251.90	839.68	18637.63	8182.29	236.37	793.84	56806.16	237.27	101.87	71.94	1426.55	306.06	165.44	140.62	233.34	219.19	252.36	216.64	1536.83	198.08	84.45	26.99	482.70	269.17	360.84	326.85	2963.17	83.27	682.03	1370.73	3025.97	2903.94	122.03	2959.51	2945.84	2978.45	2944.44	22473.14	2648.27	1147.63	225.12	6621.71	4105.29	5550.76	4779.42	-1.35	50.04	10.36	0.23	15.41	0.00	0.00	0.07	0.48	0.97	11.04	0.24	20.61	35.61	17.98	0.52	12.48	387.49	75.37	2.08	2.14	3.47	4.96	3.11	1.95	2.35	3.18	0.90	2.55	1.02	2.26	1.56	3.34	2.91	16.39	19.87	7.00	4.87	11.07	3.03	1166.51	883.75	4131.03	11662.17	1.48	28.64
854cf333fffffff	29.09	0.00	11.64	0.42	0.71	0.17	55.69	1.36	0.92	0.00	4.75	0.00	0.00	1.86	1.54	0.00	0.00	21.91	2.06	55.92	9.70	0.26	0.45	0.00	0.73	0.81	2.87	0.00	0.00	0.00	19.87	0.00	11.62	29.34	0.00	28.51	7.31	0.47	444.97	124997.11	2613.99	25613.71	26727.67	3402.59	1301.24	6817.17	9320.58	1083.52	19605.62	8345.59	300.35	800.25	90111.25	260.60	94.14	73.84	1186.78	322.84	196.06	126.78	265.92	245.34	273.99	244.15	1668.78	208.54	71.42	30.80	551.79	249.02	424.25	286.07	2989.95	40.03	569.90	1103.36	3024.08	2954.10	69.98	2996.87	2977.63	3002.72	2975.33	19936.26	2452.77	851.80	296.60	6510.25	3080.04	5402.22	3139.30	0.20	12.15	2.44	0.03	3.86	0.03	0.00	35.46	0.13	0.97	7.66	7.12	8.13	19.10	6.68	7.52	7.23	27.40	21.43	2.57	2.82	4.59	1.60	3.61	2.10	6.34	5.15	2.28	1.19	2.46	2.09	1.85	3.69	2.61	9.10	15.04	8.30	2.73	10.62	9.26	488.70	10057.03	4373.28	733.55	1.26	25.76
854cf26bfffffff	86.61	0.03	11.31	0.45	1.55	0.05	0.00	0.00	0.00	0.00	26.70	0.00	0.00	1.28	14.76	0.00	0.00	42.15	3.20	0.00	9.70	0.56	1.65	0.00	0.00	0.00	4.17	0.02	0.00	0.00	39.97	0.06	1.39	40.10	0.03	6.05	7.13	1.08	396.25	96209.20	2246.51	23221.26	26904.49	3544.41	1304.99	6541.23	9275.57	905.15	18995.81	8243.70	253.98	775.47	68118.46	234.54	103.47	71.14	1488.73	306.07	161.57	144.50	230.30	224.49	250.73	213.32	1462.36	187.04	83.76	28.35	472.78	268.75	308.63	326.22	2963.51	88.15	677.97	1449.02	3033.09	2903.15	129.94	2959.14	2952.91	2980.37	2944.02	18385.03	2198.56	1042.13	240.22	5678.71	3653.21	3811.55	4205.38	-1.01	37.19	7.68	0.03	11.75	0.06	0.00	5.56	0.61	1.43	11.00	3.24	16.03	32.84	14.32	2.79	12.18	433.97	55.59	2.58	5.16	1.94	6.54	4.16	6.73	3.50	5.45	3.03	4.06	4.04	4.73	1.24	3.55	2.31	6.93	12.20	5.32	5.06	6.67	4.80	282.73	39240.07	4192.16	20097.19	1.36	23.98
854cd427fffffff	22.09	0.01	18.96	0.11	52.48	0.47	5.02	0.88	0.00	0.00	2.00	0.00	0.00	0.46	0.86	0.00	0.00	16.29	2.01	2.71	14.48	1.29	57.17	0.00	1.46	1.28	0.00	0.00	0.00	0.00	19.88	0.00	35.89	10.81	0.00	19.73	10.96	2.73	1325.38	402234.06	2827.30	44856.68	28199.74	2432.20	1036.10	4565.55	9498.91	1881.81	21787.25	8665.13	546.44	660.32	329765.46	258.20	100.63	75.39	1113.96	320.74	188.01	132.72	269.80	243.15	270.35	242.29	1552.83	206.43	50.82	46.64	585.22	160.69	584.00	181.21	2987.38	58.97	658.05	1004.77	3032.26	2943.04	89.22	2997.12	2974.38	2999.30	2974.09	17135.29	2201.61	649.79	413.95	6134.88	2037.68	5961.95	2135.91	0.76	5.47	1.09	1.27	1.74	0.02	0.00	53.71	0.14	0.11	3.66	10.00	3.22	13.74	2.54	9.30	3.58	24.24	11.87	8.20	10.53	7.01	8.62	4.76	2.30	1.87	3.10	0.81	4.46	1.04	2.26	4.55	6.57	2.24	3.90	10.63	3.16	3.94	5.53	4.51	75.34	1595225.56	4435.78	1880.12	1.18	44.49
854c894bfffffff	58.55	0.47	40.14	0.06	0.77	0.01	0.00	0.00	0.00	0.00	11.48	0.00	1.04	1.10	6.40	0.00	0.00	47.57	4.66	0.00	26.25	0.47	1.03	0.00	0.00	0.00	3.06	0.07	0.17	0.00	38.40	0.28	1.37	48.14	0.01	1.36	6.57	0.57	364.90	57544.95	2366.83	18064.62	25749.09	4144.27	1512.92	5676.37	9141.82	731.84	17590.90	7959.48	205.70	903.83	43947.19	254.03	123.49	73.91	1311.16	336.07	170.09	165.98	261.11	238.96	268.72	234.95	1594.73	253.72	47.75	49.04	592.34	149.99	476.86	177.19	2978.84	107.76	679.28	1506.38	3062.25	2903.70	158.55	2992.30	2959.55	2998.52	2959.55	14798.39	2238.43	391.05	498.24	5405.54	1368.51	4681.29	1368.51	-0.65	26.64	5.42	0.04	8.31	0.01	0.00	0.68	0.33	1.31	12.41	2.67	16.89	33.07	15.66	3.46	13.53	287.43	50.94	4.13	4.94	4.43	14.27	5.63	5.61	5.28	10.91	3.62	2.61	4.00	4.62	0.79	1.60	1.72	12.40	5.01	2.58	1.45	2.09	2.30	794.02	17159.52	4296.91	9588.47	1.25	53.22
854cf20ffffffff	27.62	0.00	14.48	0.00	0.83	0.05	55.54	0.27	1.22	0.00	6.11	0.00	0.00	0.87	2.89	0.00	0.00	23.06	1.95	54.07	8.02	0.00	1.05	0.00	1.13	0.84	3.25	0.01	0.00	0.00	36.54	0.10	0.00	43.97	0.05	3.15	11.87	1.06	395.13	110471.80	2284.68	22309.71	25514.99	3933.66	1520.64	6432.70	9180.51	866.38	17945.50	8020.63	248.56	916.95	73838.94	256.09	91.88	72.16	1324.76	319.16	192.51	126.65	252.73	245.47	270.13	237.64	1793.33	265.22	96.86	30.74	611.55	345.43	373.69	429.76	2984.19	45.95	579.52	1167.99	3024.77	2945.87	78.89	2979.91	2979.58	2997.77	2968.97	18968.56	2512.36	1137.18	261.14	6252.05	3752.89	3912.78	4067.16	-0.34	21.50	4.28	0.06	6.81	-0.01	0.00	6.47	0.81	1.60	13.80	3.86	13.71	27.33	12.29	6.40	13.72	55.26	21.03	2.25	1.53	2.24	2.99	4.56	2.59	1.72	8.97	1.78	1.30	1.10	3.18	6.45	4.51	3.99	2.64	8.79	2.97	33.01	2.62	0.79	212.24	17270.73	4322.31	993.51	1.29	32.78
854cc6cffffffff	51.59	0.00	46.80	0.68	0.35	0.00	0.02	0.55	0.00	0.00	1.52	0.00	0.00	1.56	1.72	0.00	0.00	56.12	2.06	0.00	35.36	0.16	0.38	0.00	0.00	1.12	3.08	0.03	0.08	0.00	34.38	0.19	3.30	37.59	0.01	7.54	12.21	1.59	340.98	86052.04	1966.51	22392.62	26868.58	3580.30	1302.51	7114.72	9282.47	874.78	18970.45	8252.49	242.71	777.69	58982.15	260.48	87.06	70.93	1268.31	320.45	198.60	121.85	267.24	243.80	274.81	242.67	1370.26	186.79	58.01	34.00	446.77	193.69	378.13	219.75	2986.08	49.37	610.82	1132.17	3026.13	2945.50	80.63	2992.27	2971.13	2999.60	2971.13	16135.25	2181.15	698.93	306.42	5125.68	2480.75	4381.54	2480.75	-0.32	24.65	4.98	0.06	7.71	-0.02	0.00	13.01	0.45	0.98	11.26	4.22	13.00	28.73	11.37	4.98	12.02	96.19	30.93	1.85	3.45	6.94	2.88	11.80	3.85	8.18	7.38	4.66	2.88	14.33	4.00	2.84	2.87	1.15	4.28	6.04	5.44	1.80	2.37	1.00	579.79	3494.78	4315.26	7236.22	1.22	27.01
854cc603fffffff	49.93	0.45	38.02	0.05	8.79	1.91	0.10	0.74	0.00	0.00	4.97	0.00	0.00	2.48	1.85	0.00	0.00	32.40	4.83	0.00	45.61	0.89	6.49	0.00	0.00	0.49	0.10	0.00	0.00	0.00	5.50	0.00	38.08	4.42	0.00	40.29	10.90	0.72	523.63	169061.96	2129.36	28935.47	27334.65	3184.78	1222.69	6348.97	9306.22	1150.07	19859.96	8374.11	323.36	737.72	124072.60	263.49	78.50	70.76	1199.77	318.05	207.76	110.29	270.13	247.60	277.19	247.03	1163.87	144.65	55.01	29.02	374.38	180.34	306.36	213.00	2990.75	29.30	505.54	1040.70	3019.14	2961.47	57.67	2997.48	2976.79	3003.18	2976.79	12953.60	1650.51	566.71	297.21	4175.48	2032.09	3545.25	2032.09	0.98	3.63	0.72	0.01	1.13	0.00	0.00	80.48	0.02	0.00	0.73	4.98	0.79	6.31	0.69	5.12	0.87	29.77	34.81	1.35	2.47	1.37	1.46	6.79	5.57	4.81	8.52	5.96	0.45	4.51	2.16	3.84	4.51	3.99	5.67	7.12	6.12	8.22	5.38	9.72	419.46	30428.55	4461.32	4271.24	1.25	31.15
854cc613fffffff	16.72	0.01	8.01	0.02	6.41	0.57	66.25	1.16	0.87	0.00	1.76	0.00	0.00	1.48	0.26	0.00	0.00	8.13	1.16	66.97	10.91	0.19	7.09	0.00	0.82	1.23	0.00	0.00	0.00	0.00	1.09	0.00	41.87	0.60	0.00	44.28	10.79	1.37	816.64	387438.81	2505.02	42881.76	27693.68	2655.02	1090.70	6232.73	9456.60	1663.49	21072.68	8564.11	482.91	697.07	272423.38	264.52	76.74	70.23	1198.87	318.18	209.64	108.54	270.88	248.70	278.42	248.07	1181.25	146.47	54.32	29.92	379.84	180.47	308.08	212.04	2992.01	27.79	492.15	1046.65	3019.71	2963.58	56.13	2998.95	2978.09	3004.76	2978.09	12503.45	1553.41	551.50	286.10	4037.44	2008.81	3366.18	2008.81	1.03	2.65	0.49	0.02	0.86	0.02	0.00	88.41	0.03	0.10	0.91	4.29	0.19	1.79	0.15	3.70	0.45	11.05	15.99	0.43	0.66	1.60	6.50	6.90	12.01	5.72	25.50	6.42	1.09	2.52	0.92	1.79	2.57	3.66	4.25	5.53	2.63	3.33	3.22	2.76	167.41	28459.05	4466.07	571.28	1.21	27.76
854cd513fffffff	14.07	0.00	22.20	1.90	10.33	0.18	49.65	0.81	0.86	0.00	2.43	0.00	0.00	1.04	0.20	0.00	0.00	7.24	3.43	46.44	7.69	15.83	10.84	0.00	2.54	2.33	0.00	0.00	0.00	0.00	0.99	0.00	41.32	0.72	0.00	44.07	11.79	1.11	872.38	259910.01	2847.94	35373.51	27293.22	2872.22	1131.75	5517.76	9399.19	1479.04	20689.28	8514.43	432.57	716.97	202329.23	259.11	99.48	73.24	1171.77	322.65	187.72	134.93	269.88	243.58	272.50	242.68	1181.00	164.12	33.58	48.53	449.19	107.17	420.67	126.93	2992.48	34.55	539.99	1015.65	3025.95	2962.15	63.80	3002.86	2979.31	3005.02	2979.31	13527.33	2055.10	395.30	484.77	5037.87	1350.71	4479.62	1350.71	1.15	2.68	0.52	0.15	0.85	0.02	0.00	86.87	0.00	0.03	0.50	4.92	0.29	2.63	0.32	4.21	0.24	12.80	8.94	3.15	1.41	0.66	2.05	0.80	0.76	1.96	6.55	1.89	0.85	1.54	4.93	2.40	4.38	5.19	3.09	12.78	16.13	19.90	5.67	3.92	289.29	232017.53	4466.65	962.56	1.16	51.71
854cd5b7fffffff	45.48	0.21	3.76	0.00	0.73	0.04	49.77	0.01	0.00	0.00	24.80	0.00	0.00	4.17	2.27	0.00	0.00	13.84	1.15	46.10	2.46	0.20	1.06	0.00	3.13	0.82	0.00	0.00	0.00	0.00	9.51	0.00	50.17	7.17	0.00	25.17	6.77	1.21	300.35	81129.03	1561.80	17846.74	23835.47	4713.47	1956.30	6548.10	8973.44	698.70	16160.13	7563.12	195.83	1187.71	55949.84	263.95	98.55	72.96	1226.02	329.20	195.06	134.14	275.94	247.72	277.65	246.60	1045.39	128.59	50.92	32.30	359.42	156.63	325.41	175.39	2995.27	51.85	626.48	1040.07	3037.58	2955.08	82.50	3000.91	2981.80	3008.38	2981.80	11083.59	1625.56	362.49	431.19	3938.02	1305.90	3374.11	1309.67	0.89	4.67	0.95	0.04	1.44	0.00	0.00	71.18	0.07	0.03	0.27	7.66	0.54	11.33	0.77	7.14	1.01	36.20	41.63	1.23	2.45	1.11	1.49	4.65	3.00	3.34	4.54	5.03	0.80	2.68	1.56	4.48	8.06	8.77	3.73	7.33	10.25	8.15	13.26	4.09	1628.01	2359.30	4454.84	1521.65	1.52	44.87
854cf353fffffff	30.65	0.00	8.07	0.00	0.23	0.17	60.81	0.08	0.00	0.00	14.86	0.00	0.00	0.34	9.87	0.00	0.00	11.87	0.51	59.54	1.05	0.03	0.14	0.00	1.06	0.72	3.11	0.00	0.00	0.00	51.64	0.54	0.00	38.66	0.54	2.72	2.72	0.07	352.27	81983.96	2320.40	20485.31	25738.16	3996.07	1435.46	6547.10	9200.26	800.00	17841.54	8026.49	230.32	889.01	57755.53	249.62	96.68	73.76	1236.33	311.87	181.53	130.34	251.75	233.84	262.59	231.93	1696.57	199.63	89.11	24.23	520.63	285.45	433.56	345.10	2987.25	55.22	609.54	1219.72	3033.35	2942.93	90.42	2988.97	2974.59	3001.77	2971.54	20753.92	2441.09	988.67	249.35	6507.21	3550.16	5287.76	3874.80	-1.12	41.81	8.59	0.02	13.35	0.21	0.00	2.92	1.27	1.65	11.34	0.97	18.11	36.68	15.73	1.65	9.67	221.90	29.78	1.24	0.92	1.02	1.99	1.06	0.81	1.21	2.27	2.24	4.23	1.70	2.02	1.91	5.37	8.74	14.88	16.22	7.87	8.56	9.27	6.48	807.28	5189.99	4240.27	796.48	1.17	24.79
854cd403fffffff	75.16	0.12	16.91	0.16	1.51	4.56	1.53	0.04	0.00	0.00	32.50	0.00	0.00	1.56	12.64	0.00	0.00	36.32	3.53	0.00	10.52	0.76	1.11	0.00	0.41	0.65	5.80	0.05	0.00	0.00	33.47	0.90	0.00	47.36	0.24	1.56	9.82	0.80	519.74	149322.31	2783.68	25821.37	26658.14	3585.16	1348.89	6411.78	9242.79	1076.82	19090.06	8216.13	308.84	807.04	126213.81	250.18	112.81	74.32	1295.16	324.16	173.26	150.90	260.40	233.61	263.97	230.92	1914.31	232.98	89.28	30.24	628.08	277.46	610.06	299.80	2970.05	83.71	685.47	1296.37	3031.98	2909.92	122.06	2981.30	2953.71	2983.99	2951.80	19201.46	2729.83	834.75	316.44	6161.44	2887.93	5345.25	3176.18	-1.02	39.23	8.00	0.07	12.27	-0.01	0.00	2.15	0.59	1.42	13.00	1.20	17.67	31.64	16.16	2.14	14.03	222.08	63.53	2.32	1.52	2.73	2.92	3.83	2.00	4.25	4.98	5.05	7.63	5.55	9.92	4.58	5.56	4.09	5.98	8.46	4.44	7.35	3.87	2.98	597.39	43196.30	4138.22	2136.73	1.20	27.68
854cd093fffffff	72.77	0.04	12.17	0.07	14.05	0.70	0.17	0.02	0.00	0.00	17.26	0.00	0.00	0.69	11.56	0.00	0.00	46.11	1.16	0.00	7.61	0.45	15.08	0.00	0.00	0.08	3.05	0.02	0.00	0.00	40.54	0.11	0.00	36.55	0.04	5.69	12.21	1.79	598.09	179732.00	2619.76	29049.17	27185.23	3314.62	1251.30	6092.87	9317.19	1176.26	19787.27	8346.24	336.08	759.08	137521.55	257.11	100.35	76.50	1037.52	319.38	189.10	130.28	265.17	243.32	268.86	242.25	1627.03	229.67	52.35	50.09	608.64	169.57	554.89	180.51	2981.41	73.94	708.72	1007.06	3032.74	2928.50	104.24	2991.15	2968.27	2993.10	2967.32	16350.26	2228.78	621.58	418.80	5746.13	1976.81	5396.93	2121.35	-0.36	22.61	4.49	0.19	7.20	0.00	0.00	7.36	0.70	1.50	14.25	2.47	14.54	27.60	12.04	4.78	14.76	93.60	44.90	2.94	2.29	3.13	6.87	7.80	2.26	2.85	5.57	3.20	8.29	2.15	3.88	3.35	4.58	4.78	4.39	9.11	3.83	4.90	5.43	8.40	128.81	308050.36	4316.17	4738.49	1.10	41.65
854c89a7fffffff	68.68	0.62	16.24	4.37	9.96	0.12	0.00	0.00	0.00	0.00	0.10	0.00	0.00	0.00	0.11	0.00	0.00	44.95	22.60	0.00	17.88	3.44	10.91	0.00	0.00	0.00	0.08	0.00	0.00	0.00	20.43	0.00	21.50	18.27	0.00	30.51	5.91	3.31	591.71	132807.54	2071.00	26960.43	27464.63	3201.53	1187.56	5100.54	9324.02	1061.47	19756.61	8366.68	295.89	719.82	93053.25	251.92	100.51	69.94	1523.85	322.51	179.81	142.70	248.53	232.79	269.06	230.12	1233.49	163.71	67.12	27.88	379.68	212.56	287.67	256.79	2978.99	92.87	681.24	1462.12	3052.88	2916.72	136.17	2975.59	2979.32	2996.28	2959.33	13701.61	1732.41	750.53	266.42	4198.97	2644.60	2677.32	2980.46	0.71	7.62	1.55	0.12	2.42	-0.01	0.00	36.50	0.37	0.17	3.66	13.94	5.01	20.30	4.62	8.47	6.97	192.96	14.80	3.24	5.77	1.72	9.89	2.92	3.96	2.82	5.18	2.21	3.59	3.80	7.41	4.00	10.04	3.96	4.96	9.04	5.15	3.53	2.35	4.46	104.58	210235.75	4402.85	14212.73	1.30	27.90
854cd553fffffff	14.37	0.20	13.08	0.09	12.96	0.47	58.83	0.00	0.00	0.00	4.01	0.00	0.00	0.47	0.27	0.00	0.00	5.98	2.27	56.08	12.44	1.99	13.08	0.00	2.16	1.25	0.00	0.00	0.00	0.00	0.00	0.00	71.73	0.00	0.00	23.27	4.83	0.17	743.39	268642.91	2766.03	36258.89	26985.18	2883.80	1213.16	6126.68	9416.81	1448.15	20548.09	8474.77	422.44	774.20	206289.24	257.38	98.64	74.42	1127.54	318.98	187.16	131.82	268.14	242.06	269.89	241.60	1249.23	172.55	41.35	46.21	495.72	131.30	465.37	152.53	2990.10	38.18	570.45	981.44	3024.22	2957.52	66.70	3000.12	2977.35	3001.96	2977.35	15926.57	2086.11	578.82	423.82	5802.76	1841.81	5511.35	1841.81	0.78	2.74	0.55	0.00	0.88	0.07	0.00	84.90	0.00	0.00	0.24	5.82	0.83	4.23	0.28	3.55	0.14	22.93	13.82	3.64	5.25	15.14	5.83	6.34	0.20	8.61	1.53	1.55	2.54	0.61	1.94	3.27	2.79	5.21	6.66	4.58	3.79	6.78	10.40	3.33	88.11	226875.07	4468.35	816.91	1.19	44.84
854cf24bfffffff	57.65	0.01	29.54	0.05	3.48	0.20	8.60	0.27	0.20	0.00	10.81	0.00	0.43	0.46	5.04	0.00	0.00	35.97	7.32	7.85	22.50	4.01	4.35	0.00	0.57	0.67	2.15	0.00	0.21	0.00	36.24	0.94	1.49	36.83	0.33	12.90	8.69	0.22	435.20	92251.52	2975.51	21587.59	26043.55	3899.16	1441.82	6175.61	9268.31	896.43	18575.69	8154.17	258.81	879.90	76497.51	248.21	101.46	70.66	1512.71	319.99	177.43	142.55	236.37	264.29	265.10	228.01	1702.10	254.42	70.46	40.65	664.98	239.00	254.48	534.26	2984.11	52.08	580.62	1314.63	3031.74	2942.39	89.35	2973.83	2999.24	2999.95	2967.43	16939.47	2257.49	823.23	314.57	5853.09	2602.33	2742.14	4199.92	-0.51	27.48	5.81	0.05	8.66	0.03	0.00	14.94	0.23	0.81	8.52	1.66	13.48	36.79	11.72	3.41	8.42	139.69	38.24	3.70	4.06	2.27	7.27	1.92	2.90	3.86	3.28	4.36	2.00	2.91	5.89	2.23	4.02	6.39	5.12	7.18	3.69	19.60	3.07	4.26	299.19	72495.94	4298.90	4963.85	1.54	37.49
854cd09bfffffff	85.52	0.26	7.90	0.21	1.31	0.55	4.24	0.01	0.00	0.00	51.38	0.06	0.00	1.27	12.41	0.06	0.00	20.30	1.27	0.00	7.50	0.79	0.99	0.00	2.57	1.40	4.22	0.17	0.43	0.28	35.94	2.87	0.10	41.19	1.94	2.17	10.15	0.53	398.21	115533.89	2107.77	23737.10	26150.94	3709.96	1400.54	6765.93	9265.53	920.28	18419.75	8137.09	262.81	864.09	79218.42	245.27	107.00	77.04	1041.62	311.56	173.66	137.91	254.64	231.21	257.14	230.03	1340.86	189.58	41.71	50.82	504.98	134.39	461.79	136.07	2971.46	67.33	681.55	1038.12	3019.81	2921.20	98.62	2981.63	2957.54	2983.53	2957.34	17191.67	2403.49	609.89	448.69	6225.78	1993.01	5612.05	2060.11	-1.28	60.55	12.21	1.30	18.89	-0.05	0.01	3.88	0.55	1.15	12.11	0.53	17.99	32.63	16.60	2.16	12.40	300.53	65.96	1.25	2.33	1.91	5.93	9.29	2.31	4.28	4.77	5.10	5.21	2.00	2.88	3.74	4.72	2.94	3.04	7.23	4.87	5.06	11.10	10.06	500.76	39399.52	4059.24	2872.68	1.03	45.78
854cd653fffffff	30.48	1.33	63.29	3.89	0.97	0.04	0.00	0.00	0.00	0.00	3.08	0.45	0.51	0.13	7.69	2.19	0.00	49.79	7.05	0.00	25.35	3.15	0.60	0.00	0.00	0.00	3.07	0.00	0.35	0.00	35.63	0.80	0.00	52.82	0.25	0.09	6.79	0.20	494.57	94237.96	2143.86	23355.37	27027.74	3479.95	1271.65	5199.64	9286.27	897.57	19105.82	8273.10	254.27	761.48	65863.41	213.43	140.01	76.34	1261.92	298.31	116.18	182.14	223.70	194.48	226.46	194.48	1296.85	210.19	18.77	59.18	530.47	79.01	393.99	79.01	2943.96	96.52	707.98	1092.13	3011.60	2875.28	136.32	2954.27	2929.68	2957.99	2929.68	12914.85	2187.91	223.45	563.58	4902.41	838.33	4115.30	838.33	-1.03	45.32	9.58	0.09	14.02	-0.06	0.00	0.37	0.12	0.86	7.56	0.62	18.36	44.85	16.41	1.65	9.19	907.96	22.75	3.55	3.65	3.41	6.51	4.94	3.49	3.58	5.45	4.40	4.27	4.51	9.02	2.01	1.88	1.08	8.42	9.95	6.11	4.52	3.74	5.50	818.52	13090.11	4146.75	12743.59	1.39	60.56
854cd46ffffffff	92.24	0.18	7.26	0.01	0.02	0.03	0.26	0.00	0.00	0.00	74.05	0.00	1.04	1.21	12.12	0.00	0.00	10.74	0.06	0.00	0.61	0.02	0.00	0.00	0.08	0.06	2.85	0.19	2.07	0.33	33.44	10.96	0.00	29.56	9.50	0.00	8.68	2.40	441.47	113909.00	2192.25	25654.73	27386.09	3257.51	1213.37	6450.70	9356.17	996.44	19760.86	8386.68	281.32	732.04	81401.47	201.87	125.17	77.40	1158.79	278.08	117.33	160.75	210.90	186.69	214.33	184.58	1355.00	198.20	42.89	45.54	488.49	144.17	442.72	154.82	2936.30	68.59	666.53	1144.37	2986.19	2883.46	102.72	2947.43	2920.97	2949.45	2920.89	20454.21	2949.54	737.06	425.34	7197.55	2525.85	6410.40	2548.16	-2.12	110.02	21.71	1.90	34.24	0.00	0.02	0.00	0.95	1.02	12.85	0.00	20.50	31.38	20.28	0.00	13.01	906.74	79.99	1.34	1.87	1.79	4.74	5.22	1.62	5.76	3.94	2.96	6.16	3.25	5.23	0.64	1.65	5.72	6.34	9.97	4.45	3.94	12.07	11.36	2073.52	5743.93	3703.01	4843.30	1.05	44.71
854cd4d3fffffff	63.99	1.04	26.57	5.36	2.98	0.03	0.01	0.02	0.00	0.00	1.00	0.00	0.00	0.11	0.82	0.00	0.00	45.10	39.16	0.00	6.98	3.96	2.62	0.00	0.00	0.25	0.00	0.00	0.00	0.00	9.55	0.00	32.19	7.03	0.00	41.21	8.42	1.59	568.77	130920.44	2355.28	26673.51	27229.39	3299.57	1257.85	5199.30	9308.03	1045.25	19690.24	8348.71	297.02	752.89	95027.23	256.52	97.95	69.93	1497.32	324.89	185.65	139.24	259.53	237.62	273.38	235.02	1356.75	177.73	73.70	25.57	404.70	229.91	347.53	282.79	2980.01	94.30	692.19	1426.36	3052.40	2916.21	136.19	2975.82	2962.58	2996.50	2960.59	13832.54	1748.87	746.84	250.94	4173.31	2666.09	2951.49	3039.60	0.95	4.19	0.83	0.03	1.33	0.00	0.00	65.49	0.13	0.03	1.60	10.23	1.36	8.67	1.23	7.96	3.29	120.15	18.42	1.44	5.45	3.10	11.08	2.56	4.42	3.75	5.74	2.41	4.95	5.22	4.72	5.61	9.59	1.40	4.65	9.22	5.93	6.15	1.88	0.73	166.75	46478.79	4426.30	11593.67	1.30	26.14
854cc657fffffff	17.76	0.03	68.02	4.01	10.11	0.05	0.01	0.01	0.00	0.00	0.24	0.00	0.00	0.30	0.16	0.00	0.00	16.24	3.74	0.00	59.06	9.71	10.53	0.00	0.00	0.02	1.67	0.00	0.00	0.00	15.59	0.00	19.69	17.21	0.00	34.87	10.32	0.64	498.55	139097.08	2140.02	26224.11	26999.21	3408.78	1290.87	6504.69	9306.21	1064.95	19451.72	8309.13	298.80	774.98	111480.78	260.98	88.68	71.00	1278.13	321.93	197.88	124.05	267.32	244.03	275.27	242.84	1356.63	173.98	60.51	30.37	435.64	200.19	379.86	234.43	2987.03	55.96	635.62	1129.61	3031.11	2943.20	87.91	2992.66	2972.30	3000.65	2972.30	14092.71	1990.86	592.23	334.37	4527.01	2061.48	3906.30	2061.48	0.43	9.69	1.97	0.02	3.05	0.00	0.00	50.22	0.07	0.45	4.34	5.94	5.63	17.03	4.22	7.49	4.60	101.03	9.30	5.22	3.88	8.57	2.73	9.29	2.56	4.54	9.32	6.87	6.64	7.85	3.11	2.55	7.18	1.55	3.43	3.44	2.14	3.19	2.39	3.55	299.53	327998.59	4416.35	5660.80	1.27	32.23
854cd41bfffffff	75.56	0.05	17.30	0.76	0.25	1.08	4.85	0.15	0.00	0.00	46.66	0.00	0.13	0.51	8.96	0.00	0.00	28.20	1.94	0.00	7.30	0.89	0.64	0.00	1.81	2.94	6.83	0.10	0.00	0.00	27.32	1.32	0.00	47.63	0.33	7.87	8.42	0.18	514.09	129770.52	2606.94	24283.21	26327.91	3704.30	1370.44	6126.82	9264.34	995.97	18701.58	8173.04	283.48	835.58	97931.92	254.67	110.81	71.26	1412.86	332.05	177.60	154.44	264.01	236.97	270.44	234.08	1843.05	255.96	92.37	29.11	595.55	285.30	528.67	318.20	2973.44	82.21	678.34	1342.76	3034.91	2913.81	121.10	2982.64	2956.58	2987.98	2954.65	18547.35	2682.83	867.03	293.39	5671.88	3075.26	4605.90	3412.26	-0.70	38.97	7.99	0.23	12.13	-0.02	0.00	11.94	0.35	1.22	10.48	1.61	15.14	29.79	14.10	5.18	10.19	185.68	68.04	2.70	1.42	3.58	4.61	5.98	3.10	3.89	5.35	1.84	4.72	3.35	8.55	2.00	4.53	1.35	6.03	10.18	6.92	8.92	8.00	2.98	615.72	19160.78	4105.15	2968.48	1.25	29.75
854cd293fffffff	23.16	0.11	0.80	0.00	0.92	0.10	74.57	0.01	0.33	0.00	15.28	0.00	1.06	0.30	2.00	0.00	0.00	4.49	0.44	74.75	0.17	0.02	1.01	0.00	0.28	0.19	0.91	0.00	0.00	0.00	48.14	0.23	0.00	45.49	0.37	1.49	2.98	0.39	492.11	211267.98	1966.05	27064.68	25096.27	3829.21	1572.84	6626.97	9171.27	1035.67	17695.07	7950.73	303.43	976.38	133449.69	250.83	101.52	75.70	972.37	312.71	179.54	133.18	259.90	238.14	262.64	236.77	1219.31	190.97	33.32	61.19	492.32	102.83	391.27	104.90	2990.79	43.57	595.27	982.12	3026.63	2954.03	72.61	3001.08	2978.22	3002.61	2977.27	9725.26	1368.81	249.90	482.29	3727.06	1002.25	3001.11	1018.26	-1.16	44.62	9.35	0.05	13.89	0.07	0.00	0.55	0.18	0.67	8.53	0.89	20.61	44.23	14.06	1.21	9.05	246.33	18.82	2.09	2.42	1.05	3.95	7.00	2.56	10.75	14.21	3.00	3.57	9.86	3.67	2.04	1.01	1.70	5.29	6.19	3.67	2.38	8.63	4.96	570.38	19262.18	4288.16	557.43	1.12	48.57
854c8933fffffff	55.97	0.10	39.43	0.06	0.97	0.06	3.37	0.03	0.00	0.00	11.26	0.10	0.66	1.70	11.15	0.55	0.00	66.03	0.61	0.00	4.05	0.22	1.06	0.00	2.02	0.59	7.31	0.01	0.00	0.00	39.24	0.56	0.02	38.29	0.43	0.11	11.27	2.78	478.10	112105.57	1719.29	23432.94	26101.99	3634.90	1353.63	5409.45	9272.30	895.14	18462.77	8155.20	253.92	846.76	69552.89	240.12	112.91	70.04	1548.09	320.06	160.00	160.06	245.82	220.66	257.77	217.94	1212.66	198.11	56.85	38.46	413.91	202.46	239.54	216.98	2965.97	91.33	668.68	1470.22	3040.14	2903.67	136.47	2970.18	2946.70	2984.14	2946.50	15910.63	2466.13	704.77	365.63	5522.37	2594.53	3461.91	2596.30	-1.06	43.06	8.67	0.62	13.68	0.02	0.00	0.84	0.94	2.34	15.63	0.11	17.41	28.29	16.37	1.19	16.88	455.62	49.03	7.20	0.85	0.82	6.05	2.47	9.58	4.94	14.36	3.28	1.81	7.99	4.26	2.11	1.61	2.15	3.06	3.76	17.28	0.90	5.00	0.52	534.42	19665.16	4147.04	3768.74	1.72	39.63
854cd667fffffff	54.25	5.99	17.48	6.85	6.14	1.22	5.58	0.55	1.93	0.00	22.02	0.21	0.26	1.32	1.69	0.36	0.00	20.97	19.88	4.13	4.81	10.41	6.45	0.14	1.13	6.22	0.84	0.00	0.00	0.00	22.17	0.17	12.74	27.73	0.04	27.02	8.81	0.47	680.66	177437.56	2266.65	29493.03	26792.05	3314.95	1287.25	5392.10	9351.10	1156.09	19728.90	8335.90	331.88	801.77	132384.51	259.24	104.04	75.51	997.25	323.55	186.69	136.86	268.61	246.66	271.48	244.88	1109.58	172.55	27.18	60.50	459.55	85.92	372.82	95.55	2988.95	73.00	690.88	1043.38	3041.33	2935.92	105.41	2999.45	2975.05	3001.45	2974.74	8831.71	1210.65	218.87	470.00	3304.56	866.84	2662.85	904.77	0.49	17.40	3.59	1.02	5.44	-0.01	0.00	39.73	0.19	0.47	4.68	3.22	9.66	25.85	7.13	3.44	5.62	124.77	33.31	0.78	1.84	0.67	4.88	4.00	1.54	3.21	2.97	2.24	1.83	4.12	3.52	16.03	11.34	4.82	1.78	7.31	4.13	5.20	6.25	11.54	748.49	92781.77	4383.93	3495.53	1.08	52.62
854c898ffffffff	76.21	3.87	10.01	8.92	0.78	0.18	0.02	0.00	0.00	0.00	35.79	0.26	4.11	1.22	10.42	0.20	0.00	32.97	2.33	0.00	3.65	8.15	0.89	0.00	0.00	0.00	3.00	0.02	0.09	0.00	36.55	0.84	0.09	42.65	0.49	10.53	5.57	0.18	365.98	72339.56	2647.03	19816.28	25965.76	3989.24	1453.98	6263.15	9222.45	797.02	18241.99	8090.74	224.83	881.81	53492.50	240.75	114.51	71.41	1583.35	321.06	161.87	159.20	236.16	253.65	258.60	218.69	1415.63	190.25	54.65	34.08	472.65	232.03	244.11	324.87	2974.06	87.42	645.26	1561.69	3048.41	2913.04	135.37	2977.72	2991.91	2993.87	2954.64	14851.66	2070.57	761.75	311.92	4618.99	2695.23	2743.99	3114.99	-1.06	44.94	9.31	0.03	13.99	0.03	0.00	7.10	0.22	1.08	9.51	1.46	16.17	35.91	16.00	2.21	10.34	400.69	62.90	3.17	3.24	2.23	5.49	3.95	6.31	6.45	7.08	5.94	4.50	3.14	9.55	2.83	2.80	1.47	4.77	6.70	8.19	6.68	1.55	3.99	476.72	7527.25	4148.29	6992.92	1.70	36.35
854cd2d7fffffff	84.91	1.73	13.19	0.02	0.15	0.00	0.00	0.00	0.00	0.00	38.60	3.82	0.81	1.53	14.83	4.40	0.00	29.40	4.84	0.00	1.19	0.38	0.19	0.00	0.00	0.00	2.15	0.00	0.00	0.00	48.45	1.76	0.00	36.46	2.09	0.06	8.21	0.82	350.78	62208.97	2240.18	18898.45	26052.67	4031.22	1440.92	6127.35	9196.69	754.11	17877.77	8027.53	212.66	866.57	45823.18	200.50	118.39	76.76	1075.90	270.99	117.71	153.27	210.51	185.08	212.39	184.82	1668.09	246.54	46.14	56.67	697.72	148.40	593.90	148.41	2940.67	64.37	665.12	1055.07	2988.54	2892.02	96.52	2951.18	2926.59	2953.13	2926.39	12259.73	1588.90	378.56	448.01	4611.43	1338.44	3932.11	1388.20	-1.45	58.21	11.98	0.08	18.04	0.03	0.01	0.63	0.43	0.85	10.54	0.19	19.77	39.20	15.68	0.92	11.78	929.52	61.90	4.51	2.54	0.80	2.74	2.56	2.51	4.22	5.05	4.21	2.47	3.20	7.95	3.11	2.83	8.86	5.81	11.48	4.04	6.61	9.30	5.21	1945.28	5713.42	4065.20	15759.29	1.18	45.14
854c89bbfffffff	64.63	1.08	20.38	7.82	5.04	0.95	0.09	0.01	0.00	0.00	18.41	0.17	0.14	0.48	6.86	0.08	0.00	33.61	8.20	0.00	16.63	9.10	6.06	0.00	0.00	0.26	2.07	0.12	0.30	0.00	28.52	1.15	2.35	34.12	0.51	24.63	5.90	0.33	524.32	120225.82	2749.38	25176.41	27192.63	3433.90	1253.45	5800.15	9339.35	1049.72	19733.59	8367.58	294.06	750.81	91545.44	241.34	111.53	71.20	1568.44	319.45	164.00	155.45	238.76	246.72	258.97	219.35	1319.55	184.99	58.01	33.75	428.00	222.46	244.67	289.99	2974.10	89.93	659.02	1539.10	3049.09	2912.76	136.32	2975.93	2992.45	2993.42	2954.57	13790.85	1883.67	713.83	306.62	4287.14	2467.52	2476.36	2941.84	-0.53	37.81	7.92	0.19	11.82	0.03	0.00	19.49	0.11	0.60	6.80	3.67	12.37	33.72	11.67	4.73	6.83	377.47	40.71	2.25	2.66	1.92	2.05	1.76	6.88	3.54	5.85	3.00	4.50	3.79	9.10	9.05	8.06	4.21	4.63	5.38	9.11	4.33	2.84	5.10	314.39	73064.85	4184.57	4746.07	1.67	36.45
854cd40ffffffff	75.96	0.02	23.31	0.17	0.52	0.01	0.00	0.00	0.00	0.00	36.59	0.00	0.00	0.50	17.32	0.00	0.00	37.78	0.45	0.00	6.89	0.08	0.39	0.00	0.00	0.00	5.41	0.18	0.59	0.00	31.18	2.89	0.00	45.19	1.08	3.73	9.08	0.67	406.45	87047.62	2530.99	22096.82	26595.05	3700.22	1370.33	6436.01	9283.92	886.00	18906.41	8222.99	253.68	814.49	68092.64	240.60	116.20	75.85	1212.23	315.10	162.83	152.27	250.64	224.99	253.67	222.57	1877.94	230.14	80.14	33.21	624.45	256.28	610.64	267.06	2961.57	83.07	694.81	1234.24	3021.71	2902.20	119.51	2972.26	2945.77	2974.86	2944.08	19824.74	2801.30	869.63	329.24	6460.08	2905.64	5690.96	3143.39	-1.25	51.94	10.54	0.04	16.22	0.02	0.01	3.44	0.62	1.35	12.76	1.22	17.33	30.71	16.49	2.68	13.39	351.69	62.17	3.18	3.48	3.70	3.53	4.50	2.59	4.44	5.36	2.59	4.76	2.24	3.70	1.77	5.60	3.57	7.33	14.05	5.49	5.42	7.34	5.37	945.70	7015.07	4019.88	11240.72	1.04	28.29
854cc67bfffffff	73.76	1.28	24.18	0.18	0.48	0.02	0.09	0.00	0.00	0.00	29.04	0.00	0.00	2.80	5.79	0.00	0.00	29.22	4.59	0.13	25.46	2.44	0.54	0.00	0.00	0.00	0.00	0.00	0.00	0.00	5.48	0.00	44.58	3.71	0.00	35.58	8.97	1.67	275.69	57701.72	1552.15	16644.54	23916.21	4683.11	1924.30	6614.57	8912.28	645.87	15834.34	7466.07	180.27	1190.92	39317.90	263.82	98.16	73.04	1235.30	329.39	195.90	133.48	275.75	249.70	277.40	246.15	1206.23	150.55	59.91	31.37	407.10	187.73	355.07	213.82	2991.81	47.47	607.47	1051.21	3031.31	2953.55	77.76	2997.28	2978.09	3004.80	2978.09	12321.54	1774.01	457.27	388.44	4220.49	1611.99	3635.41	1611.99	0.91	3.76	0.76	0.10	1.20	0.00	0.00	78.89	0.06	0.00	0.31	6.36	0.55	6.70	0.51	5.37	1.25	59.83	73.09	2.99	3.16	1.90	3.85	6.70	3.07	6.54	6.71	8.09	1.99	7.33	6.19	5.65	6.88	4.15	3.15	3.49	4.11	3.14	5.95	4.95	2957.93	8305.59	4463.87	8128.89	1.61	41.40
854cd467fffffff	97.10	0.00	2.75	0.00	0.14	0.01	0.00	0.00	0.00	0.00	74.79	0.00	0.01	1.10	16.24	0.00	0.00	7.60	0.01	0.00	0.14	0.00	0.11	0.00	0.00	0.00	5.24	0.04	0.49	0.03	41.92	2.36	0.00	37.33	1.04	0.00	9.84	1.71	302.25	66274.80	1867.19	19498.44	26113.96	3959.53	1434.17	7059.64	9199.70	772.27	18037.38	8069.47	212.68	856.38	45456.25	230.83	116.25	77.36	1104.23	302.56	153.26	149.30	239.70	216.43	242.87	214.37	1514.92	205.05	53.08	43.37	541.07	170.57	508.89	174.69	2957.92	79.98	704.26	1117.27	3013.87	2900.36	113.51	2967.96	2943.25	2970.46	2942.29	19424.77	2747.11	767.66	395.12	6608.48	2505.96	6134.84	2655.56	-1.69	63.39	12.95	0.11	19.81	0.04	0.01	0.00	0.69	1.32	13.73	0.01	19.52	32.45	18.31	0.01	13.96	487.18	85.24	1.37	1.68	1.16	5.24	5.47	2.79	4.85	4.52	2.32	5.09	1.74	2.90	2.40	4.63	3.11	6.94	12.00	6.69	3.13	12.16	9.83	1167.57	9490.73	4044.47	7111.33	0.98	38.43
854cf323fffffff	79.47	0.00	20.51	0.00	0.02	0.00	0.00	0.00	0.00	0.00	16.02	0.00	0.00	7.78	4.24	0.00	0.00	62.38	1.36	0.00	8.20	0.02	0.00	0.00	0.00	0.00	6.62	0.33	0.00	0.00	31.79	2.19	0.00	45.50	1.25	1.90	10.12	0.30	342.11	95529.78	2056.41	23812.07	27228.92	3393.89	1237.06	7552.93	9294.70	916.03	19258.48	8293.71	258.32	742.66	66965.23	247.10	104.07	73.40	1278.57	316.27	175.43	140.84	255.38	230.56	261.48	229.08	1823.10	236.25	79.17	31.55	591.41	265.69	516.78	304.25	2977.43	55.15	622.39	1190.24	3021.42	2933.01	88.42	2983.69	2961.80	2991.38	2961.80	22044.07	2997.19	888.14	339.47	7232.50	3162.50	5958.24	3165.43	-1.45	54.30	11.02	0.03	16.92	0.01	0.01	0.26	0.37	1.49	13.02	0.65	18.29	32.44	17.60	1.55	14.33	249.85	60.51	4.78	3.08	5.04	5.46	5.75	3.66	5.28	8.68	6.50	5.85	5.93	5.84	2.23	2.74	1.61	6.91	11.71	2.69	1.56	2.96	1.74	1129.06	104.27	4049.57	9588.35	1.27	30.70
854c89b7fffffff	84.80	0.01	14.67	0.05	0.48	0.00	0.00	0.00	0.00	0.00	31.70	0.00	0.05	0.31	18.63	0.00	0.00	39.02	0.55	0.00	9.25	0.26	0.22	0.00	0.00	0.00	3.55	0.04	0.07	0.00	47.65	0.48	0.00	41.82	0.31	0.02	5.54	0.53	351.34	71153.90	2453.96	20223.62	26227.21	3885.66	1439.03	6526.29	9187.32	810.18	18356.60	8106.50	228.43	862.45	52077.35	223.28	108.44	71.53	1511.71	298.10	147.51	150.58	219.01	234.47	239.80	201.83	1517.65	202.64	81.23	31.76	502.64	267.39	285.26	345.41	2956.77	82.81	658.51	1478.63	3024.93	2899.35	125.58	2952.47	2974.53	2974.53	2937.48	18777.88	2290.63	1106.71	257.82	5973.27	3511.55	3511.55	4506.08	-1.32	46.94	9.65	0.04	14.85	0.05	0.00	0.06	0.72	1.53	13.12	0.06	18.18	34.90	17.51	0.12	13.80	609.88	64.13	2.14	2.59	1.51	4.61	2.21	3.66	3.39	3.62	3.94	2.93	2.29	3.06	1.40	3.66	3.14	9.30	11.84	5.76	9.46	10.84	8.64	494.31	6949.36	4133.44	11537.45	1.45	26.94
854cd5cffffffff	38.32	0.05	42.86	0.83	5.59	0.57	11.73	0.04	0.00	0.00	8.21	0.00	0.00	2.23	1.59	0.00	0.00	20.23	12.92	10.14	23.38	13.92	5.30	0.00	1.30	0.78	0.00	0.00	0.00	0.00	0.43	0.00	47.10	0.35	0.00	39.73	11.67	0.72	455.36	117853.80	2422.60	23829.32	26441.22	3684.70	1367.71	6269.56	9251.24	963.87	18799.64	8177.89	273.63	832.50	87736.04	256.87	100.11	73.88	1159.70	320.00	185.35	134.65	267.74	241.26	269.76	240.43	1231.86	174.46	38.91	45.16	475.00	136.52	451.88	148.75	2989.93	38.62	569.05	996.23	3024.57	2956.90	67.67	3000.01	2977.00	3002.05	2977.00	15477.78	2133.66	512.05	447.82	5711.84	1688.67	5346.68	1688.67	1.04	2.53	0.49	0.04	0.81	0.02	0.00	88.82	0.02	0.03	0.31	3.84	0.34	2.75	0.22	3.20	0.46	29.43	30.45	1.66	2.80	4.68	1.83	6.59	0.72	4.29	6.55	1.60	6.31	3.21	2.98	6.46	8.23	4.64	7.33	4.39	6.55	8.96	7.50	2.70	436.26	88936.43	4463.69	2244.39	1.19	47.56
854cd693fffffff	90.60	0.62	8.77	0.00	0.00	0.00	0.00	0.00	0.00	0.29	34.71	0.00	41.86	6.28	3.17	0.00	0.00	12.72	0.55	0.00	0.22	0.00	0.00	0.00	0.00	0.19	1.14	0.07	1.80	0.00	33.74	10.74	0.00	36.89	8.10	0.00	6.86	0.68	465.61	40006.25	3250.92	14797.64	24688.23	4740.16	1703.06	3978.04	9149.96	637.96	16795.27	7778.61	180.16	1036.07	33844.71	125.64	119.60	73.99	1334.52	201.32	40.79	160.53	130.17	108.09	140.24	106.19	1657.00	282.68	56.02	49.95	593.80	179.17	490.96	199.66	2868.56	78.10	710.70	1045.24	2922.01	2812.18	109.84	2877.73	2853.64	2880.15	2853.64	20667.60	3045.62	617.86	445.51	7268.17	2228.14	5807.19	2228.14	-2.11	99.90	20.24	0.11	30.64	-0.06	0.01	0.00	0.19	0.59	8.98	0.00	21.78	37.95	19.54	0.00	10.95	2085.63	86.14	1.71	0.05	1.19	4.20	87.46	0.24	0.94	1.65	0.22	0.23	0.84	1.19	0.02	0.00	0.02	0.00	0.02	0.00	0.00	0.02	0.00	1987.13	0.00	3771.15	13325.45	1.73	43.70
854cf343fffffff	23.45	0.00	14.41	5.07	0.65	0.00	28.95	13.51	13.96	0.00	21.13	0.11	0.00	1.07	1.64	0.00	0.00	12.88	7.55	30.50	11.17	0.04	0.79	0.00	0.23	12.90	0.01	0.00	0.00	0.00	4.94	0.00	28.91	7.49	0.00	42.27	15.62	0.76	542.17	154831.17	2993.79	27321.40	27018.72	3361.42	1279.64	6194.26	9347.97	1142.25	19744.46	8372.32	329.92	769.30	117197.80	261.90	95.17	74.82	1135.21	321.94	195.51	126.42	271.04	247.29	273.66	245.64	1928.60	199.70	108.84	18.91	568.51	351.22	560.89	428.16	2989.97	68.61	653.44	1226.68	3043.91	2939.00	104.91	2996.06	2974.38	3004.27	2973.53	18654.93	2232.13	815.75	261.95	5699.72	2995.36	4992.47	3453.23	1.21	2.63	0.51	0.02	0.85	0.00	0.00	82.77	0.04	0.09	1.68	3.07	1.62	4.80	1.06	3.66	1.20	7.39	46.12	0.82	2.02	1.05	7.56	2.69	3.49	2.41	4.10	0.66	2.04	1.88	3.61	1.43	2.60	10.03	9.35	26.64	3.11	1.41	3.02	10.07	1544.47	8044.63	4450.98	1348.33	1.19	24.55
854cd47bfffffff	79.12	0.17	18.85	0.43	0.91	0.50	0.01	0.00	0.00	0.00	46.09	0.00	2.85	0.55	20.37	0.04	0.02	22.16	0.87	0.00	5.53	0.94	0.56	0.00	0.00	0.02	3.94	0.10	0.13	0.00	38.51	2.34	0.00	38.77	1.88	4.02	9.32	0.98	443.60	93542.18	2246.70	22911.32	26941.20	3581.44	1268.78	5711.59	9299.25	899.34	18975.84	8251.50	251.08	772.39	63860.07	197.95	133.60	77.12	1234.64	279.54	107.50	172.04	207.44	182.28	210.97	179.44	1349.43	197.98	44.98	40.10	469.23	158.49	412.78	178.53	2931.86	74.06	677.15	1191.68	2985.33	2876.04	109.30	2942.62	2916.29	2945.29	2915.63	20771.09	2973.22	827.13	349.07	6681.48	2973.62	5838.32	3171.47	-1.41	64.94	13.12	0.36	20.23	-0.01	0.01	5.28	0.61	1.14	12.73	0.63	18.73	28.55	17.57	1.70	13.06	965.51	64.20	3.66	2.27	2.89	4.56	1.51	1.40	6.00	6.10	2.43	3.78	1.45	4.12	1.61	4.51	7.08	12.00	12.47	3.98	2.93	9.06	6.21	900.16	8113.79	3939.87	5914.34	1.45	27.40
854cd623fffffff	56.18	7.32	2.85	1.60	0.41	0.56	29.49	0.05	1.54	0.00	22.73	0.07	0.75	2.37	6.56	0.19	0.00	17.29	14.91	27.99	2.37	1.57	0.37	0.00	1.60	1.26	1.40	0.00	0.00	0.03	38.80	0.57	0.50	39.79	0.38	15.51	2.99	0.04	457.85	103272.37	2454.35	21429.98	25771.10	3965.16	1453.99	5488.30	9255.32	870.77	17929.66	8031.27	244.54	895.57	73363.78	247.43	107.74	74.73	1058.54	313.85	170.60	143.25	256.98	233.13	259.75	231.85	969.28	154.70	20.73	63.02	410.61	70.05	332.37	71.15	2983.87	52.22	622.75	1037.44	3024.97	2941.44	83.53	2994.15	2971.02	2996.60	2969.93	10186.49	1549.29	224.25	517.78	4141.53	945.92	3272.30	1000.67	-0.79	39.66	8.20	0.03	12.28	0.08	0.00	8.54	0.07	0.75	8.05	1.86	15.71	41.47	12.77	2.27	8.51	325.92	42.41	3.72	1.87	3.03	1.80	2.86	1.59	6.92	1.74	4.43	2.85	1.63	5.85	9.05	8.53	10.21	3.07	3.29	4.42	3.82	5.89	13.43	2287.06	3762.75	4272.21	2854.50	1.19	52.97
854cd473fffffff	81.74	0.06	17.95	0.00	0.24	0.01	0.00	0.00	0.00	0.00	52.99	0.00	0.57	0.55	19.55	0.00	0.00	20.85	1.09	0.00	4.22	0.00	0.18	0.00	0.00	0.00	3.24	0.04	0.76	0.00	40.16	4.33	0.00	41.11	2.60	0.00	7.61	0.16	413.21	77382.34	2951.92	21163.57	26751.38	3711.95	1322.68	6015.44	9292.38	864.46	19039.05	8256.68	246.28	790.05	60853.39	210.39	128.50	76.44	1248.71	290.67	123.79	166.89	219.82	194.51	223.68	191.75	1506.78	208.40	57.35	35.75	509.51	193.90	458.13	212.73	2938.10	79.40	686.20	1236.99	2995.38	2879.71	115.67	2948.52	2922.09	2951.60	2920.86	22238.54	3166.95	967.76	324.68	7093.84	3343.32	6196.57	3633.20	-1.72	66.25	13.68	0.05	20.43	0.01	0.01	0.00	0.15	0.76	10.48	0.02	20.75	37.03	18.96	0.05	11.81	784.89	75.74	2.94	2.02	2.19	4.87	2.99	2.22	5.24	5.78	1.59	2.12	1.59	4.86	0.95	3.13	4.17	11.47	18.92	7.35	3.36	7.47	4.75	1337.08	3868.66	3968.12	11558.62	1.24	26.86
854c890ffffffff	55.10	0.58	41.55	0.13	0.54	0.03	2.05	0.01	0.00	0.00	11.55	0.00	2.60	1.91	18.20	0.01	0.00	48.86	4.00	0.00	9.82	0.60	0.32	0.00	0.87	1.25	3.97	0.09	0.01	0.00	43.67	1.15	0.00	37.56	0.88	0.00	10.59	2.08	457.57	79956.04	2147.37	20893.04	26419.51	3805.88	1378.28	5017.13	9257.94	799.55	18277.10	8122.24	227.64	824.48	56436.21	241.13	121.18	71.46	1491.26	324.86	156.44	168.41	247.17	222.32	258.04	219.68	1251.95	216.55	46.56	45.07	469.24	172.76	270.67	179.46	2967.41	97.01	677.01	1464.58	3044.84	2901.59	143.25	2971.03	2948.74	2986.13	2948.74	14988.75	2472.56	551.89	441.89	5615.35	1914.13	3503.04	1914.13	-1.13	46.15	9.41	0.62	14.49	0.00	0.01	0.00	0.98	1.73	13.15	0.24	19.07	32.02	17.42	0.23	15.16	485.74	48.68	4.92	3.49	1.94	16.32	3.60	5.39	7.11	10.17	2.27	3.12	4.58	11.77	1.05	1.81	0.86	11.88	3.56	1.76	2.88	0.81	0.71	759.52	7908.62	4118.92	6723.36	1.60	49.29
854cd4c3fffffff	29.74	0.46	16.77	50.02	2.79	0.13	0.03	0.07	0.00	0.00	3.11	0.00	0.03	0.07	0.48	0.00	0.00	17.95	17.43	0.00	5.29	49.15	3.68	0.00	0.00	2.81	0.41	0.00	0.00	0.00	1.99	0.02	16.74	6.51	0.00	59.78	14.34	0.21	572.72	152332.02	2083.15	28311.00	27348.64	3257.83	1222.70	5760.01	9354.16	1132.86	20044.35	8418.35	321.22	733.37	111479.81	261.52	102.59	69.89	1503.98	333.51	187.72	145.78	269.28	242.66	278.90	240.02	1441.54	204.56	76.17	27.32	444.06	238.48	378.76	280.94	2981.68	92.27	690.31	1406.45	3051.72	2918.15	133.57	2983.88	2964.45	2997.85	2962.45	14539.74	1974.17	749.76	264.50	4319.52	2691.86	3289.07	3027.79	1.46	4.10	0.84	0.16	1.28	-0.03	0.00	88.67	0.03	0.09	0.74	1.20	1.35	4.17	1.00	2.10	0.65	74.07	21.16	2.69	1.84	1.76	11.21	1.42	3.09	2.15	23.41	2.11	3.07	3.44	3.75	7.01	4.32	1.19	6.37	7.00	7.73	2.31	2.55	1.58	259.77	80950.54	4406.43	5368.68	1.35	28.28
854cc673fffffff	55.91	2.79	37.28	0.50	0.19	0.00	3.34	0.00	0.00	0.00	11.10	0.00	0.13	0.60	4.54	0.00	0.00	31.14	6.92	2.11	39.51	2.34	0.30	0.00	1.00	0.30	0.00	0.00	0.00	0.00	7.74	0.00	48.90	6.33	0.00	31.46	4.92	0.66	402.53	97470.82	2551.93	23035.66	26819.13	3559.33	1295.21	6609.15	9297.90	916.10	19218.06	8290.76	263.01	784.71	73891.49	263.01	92.07	72.61	1223.97	325.04	199.13	125.91	269.47	248.21	276.58	245.62	1241.86	154.31	61.26	29.72	408.98	196.03	348.55	228.55	2989.81	36.41	553.16	1039.72	3022.43	2957.08	65.34	2996.01	2975.95	3002.38	2975.95	12730.11	1744.70	517.02	344.63	4227.89	1822.14	3667.05	1822.14	0.82	4.61	0.96	0.08	1.47	0.00	0.00	67.93	0.09	0.00	0.32	8.84	1.06	11.74	0.96	7.60	1.45	67.82	37.62	2.40	3.37	1.03	1.67	7.36	2.83	4.53	4.48	6.78	1.01	3.63	3.34	4.67	4.00	8.24	4.85	6.87	6.37	5.74	9.96	6.90	1158.36	1437.94	4462.70	5752.23	1.53	37.85
854cd66ffffffff	69.79	22.08	7.16	0.37	0.20	0.40	0.00	0.00	0.00	0.00	32.78	1.74	1.08	0.92	8.80	1.15	0.00	23.67	29.29	0.00	0.32	0.06	0.18	0.00	0.00	0.01	1.35	0.01	0.18	0.00	37.44	2.50	0.11	49.55	1.50	3.22	3.88	0.26	396.81	70575.62	2310.20	18098.68	25262.77	4335.20	1598.18	5288.89	9146.61	725.61	17300.88	7889.08	206.11	963.56	51932.46	231.34	115.09	76.57	1056.36	300.79	151.48	149.32	241.11	216.90	243.39	215.79	1278.80	190.29	32.14	58.49	536.10	105.58	445.32	107.74	2965.52	81.51	707.78	1066.67	3022.83	2907.84	114.99	2975.86	2950.71	2977.93	2950.71	10754.09	1389.59	294.22	461.45	4016.99	1076.66	3370.83	1086.37	-0.95	50.06	10.41	0.12	15.41	-0.01	0.00	0.58	0.20	0.63	6.61	0.80	18.18	48.15	15.16	1.62	8.06	538.45	48.12	1.00	3.92	0.87	3.24	2.68	2.82	5.68	3.32	3.13	2.81	3.46	6.01	6.21	8.33	10.35	3.28	3.91	2.17	6.79	14.70	5.31	1839.11	2246.12	4089.75	6965.42	1.16	48.08
854cd697fffffff	62.33	0.79	35.35	1.45	0.05	0.03	0.00	0.00	0.00	0.08	24.66	0.10	21.77	0.51	13.28	0.30	0.02	33.41	4.87	0.00	0.89	0.10	0.02	0.00	0.00	0.00	3.24	0.05	0.99	0.00	39.05	4.89	0.00	37.95	3.82	0.00	8.36	1.65	455.91	53326.71	2673.38	17083.34	25277.26	4379.05	1600.25	4352.20	9183.48	685.68	17372.51	7920.90	196.79	964.72	40838.38	161.75	126.74	75.42	1294.21	240.22	73.30	166.92	166.80	144.88	175.55	142.80	1322.18	231.65	43.02	49.83	478.04	140.80	383.73	163.21	2903.40	75.03	697.26	1102.16	2955.21	2847.60	107.61	2913.65	2888.76	2915.57	2887.83	18851.73	2680.27	649.26	397.24	6429.97	2379.07	5044.09	2407.43	-1.84	82.69	16.79	0.31	25.63	-0.02	0.01	0.00	0.62	0.90	11.11	0.00	21.36	34.57	18.94	0.11	12.38	1518.08	53.29	33.90	0.78	1.00	10.26	8.38	4.22	2.48	15.19	2.78	3.28	2.04	5.91	0.06	0.93	0.48	3.49	2.19	0.53	0.23	0.94	0.93	1808.47	1824.43	3885.60	15780.00	1.48	48.79
854cd6affffffff	66.89	10.25	11.82	5.02	5.40	0.62	0.00	0.00	0.00	0.00	14.07	1.16	0.77	1.71	3.18	0.50	0.00	44.60	14.52	0.00	9.28	4.45	5.77	0.00	0.00	0.00	1.81	0.08	0.19	0.00	25.48	0.92	6.68	37.14	0.70	21.22	5.47	0.31	571.54	120082.67	2681.51	25499.48	27051.99	3406.11	1272.60	5373.85	9313.37	1038.69	19666.70	8348.15	295.25	764.96	88905.73	253.91	109.93	73.52	1128.76	324.01	175.67	148.34	264.18	238.70	266.96	237.27	819.71	140.67	15.98	63.56	355.23	57.37	281.15	57.98	2984.79	61.24	651.64	1014.03	3031.21	2937.40	93.81	2994.14	2972.21	2997.21	2971.51	11042.49	1620.07	279.10	498.69	4414.80	1060.28	3297.33	1195.60	-0.45	37.58	7.64	0.07	11.65	-0.08	0.00	17.25	0.10	0.59	7.49	5.17	12.35	31.07	11.52	6.24	8.22	249.32	36.48	5.93	9.63	0.92	3.76	2.11	1.46	6.26	4.37	9.16	3.74	1.75	4.77	12.52	5.73	5.84	3.80	5.55	3.61	2.00	1.84	5.24	887.08	75283.48	4181.38	8468.48	1.27	60.27
854cd58bfffffff	31.44	0.02	49.49	16.21	2.23	0.26	0.29	0.01	0.05	0.00	4.10	0.00	0.00	1.00	1.04	0.00	0.00	21.41	8.11	0.00	33.92	27.82	2.44	0.00	0.00	0.16	0.00	0.00	0.00	0.00	25.21	0.00	32.35	14.07	0.00	11.34	11.84	5.20	399.64	90477.15	2525.21	21330.24	26370.90	3900.15	1413.90	6328.02	9217.61	884.93	18361.14	8105.51	249.43	834.85	72596.59	258.53	103.39	73.55	1197.98	324.77	185.11	139.67	269.53	242.65	272.12	241.64	1291.22	169.20	42.92	44.21	474.07	133.40	438.31	152.95	2990.87	49.44	606.56	1070.38	3033.08	2951.80	81.28	3001.18	2977.08	3004.09	2977.08	14109.72	2139.87	418.89	477.39	5230.01	1444.41	4483.34	1444.41	0.49	7.24	1.44	0.36	2.31	-0.01	0.00	41.25	0.26	0.15	5.84	10.24	5.42	16.55	4.39	8.70	7.20	32.90	14.25	0.74	0.60	1.11	0.23	1.09	0.49	0.50	2.22	0.74	0.56	0.98	2.41	1.02	6.50	10.04	18.43	15.84	9.52	19.62	3.95	3.40	621.67	36319.66	4427.86	3586.77	1.20	52.40
854cd44bfffffff	67.92	0.24	21.45	7.80	2.48	0.11	0.00	0.00	0.00	0.34	28.22	0.01	28.35	2.84	9.47	0.12	0.01	24.27	1.65	0.00	0.76	1.85	2.10	0.00	0.00	0.00	3.25	0.22	0.71	0.09	34.22	6.04	0.00	41.58	4.70	1.60	6.40	1.18	531.12	77305.37	2791.54	20112.12	26044.24	3995.14	1463.78	4448.85	9232.26	806.44	18364.74	8114.43	232.07	884.41	57962.75	153.68	126.04	75.55	1282.40	231.53	65.87	165.66	161.97	136.89	167.33	134.90	1379.59	237.14	44.62	49.60	494.91	147.30	408.45	171.23	2892.03	75.44	690.36	1158.37	2944.89	2835.65	109.23	2902.11	2877.11	2904.76	2875.78	18280.02	2517.62	708.14	346.86	5961.18	2564.13	4779.86	2749.08	-1.68	76.32	15.41	0.17	23.65	-0.05	0.01	0.78	0.41	1.02	10.40	0.29	19.11	37.95	17.55	0.99	11.51	1634.25	62.26	3.58	0.22	1.04	2.27	7.41	0.69	1.82	3.81	1.34	2.17	0.35	2.14	0.13	2.31	22.72	41.31	4.33	0.45	0.46	0.46	0.99	939.08	56162.42	3896.71	11087.53	1.38	42.22

La matriz ambiental se compone de 140 variables de tipo numérico, conteniendo el valor de cada variable para cada uno de los 80 sitios. La siguiente tabla y el gráfico muestran un resumen de los estadísticos básicos de la matriz ambiental.

estad_basicos <- env %>%
  pivot_longer(everything(), names_to = "Variable", values_to = "Valor") %>%
  group_by(Variable) %>%
  summarise(
    Media = mean(Valor),
    Mediana = median(Valor),
    `Desv. Estándar` = sd(Valor),
    Varianza = var(Valor),
    `Error Estándar` = sd(Valor) / sqrt(length(Valor)))
estad_basicos %>% estilo_kable(titulo = 'Matriz ambiental', nombres_filas = F, alinear = 'crrrr')

Table 1.8: Matriz ambiental
Variable	Media	Mediana	Desv. Estándar	Varianza	Error Estándar
CGIAR-ELE mean	367.65	236.67	405.41	1.643600e+05	45.33
CGL Bare / sparse vegetation	0.02	0.00	0.12	2.000000e-02	0.01
CGL Closed forest, deciduous broad leaf	0.16	0.00	0.57	3.300000e-01	0.06
CGL Closed forest, evergreen broad leaf	21.79	17.22	18.62	3.466500e+02	2.08
CGL Closed forest, evergreen needle leaf	0.01	0.00	0.05	0.000000e+00	0.01
CGL Closed forest, mixed	2.36	0.02	7.41	5.491000e+01	0.83
CGL Closed forest, not matching any of the other definitions	1.27	1.01	1.26	1.590000e+00	0.14
CGL Cultivated and managed vegetation / agriculture	3.52	0.58	7.52	5.654000e+01	0.84
CGL Herbaceous vegetation	10.53	7.19	11.64	1.354700e+02	1.30
CGL Herbaceous wetland	0.78	0.18	1.71	2.930000e+00	0.19
CGL Oceans, seas	10.57	0.00	21.09	4.449300e+02	2.36
CGL Open forest, deciduous broad leaf	0.17	0.00	0.62	3.800000e-01	0.07
CGL Open forest, evergreen broad leaf	7.40	6.48	5.99	3.591000e+01	0.67
CGL Open forest, evergreen needle leaf	0.00	0.00	0.00	0.000000e+00	0.00
CGL Open forest, mixed	0.00	0.00	0.00	0.000000e+00	0.00
CGL Open forest, not matching any of the other definitions	28.68	28.55	14.67	2.151300e+02	1.64
CGL Permanent water bodies	0.56	0.00	1.21	1.460000e+00	0.13
CGL Shrubs	7.47	2.37	11.90	1.416900e+02	1.33
CGL Urban / built up	4.69	1.04	10.63	1.129700e+02	1.19
CH-BIO bio01 mean annual air temperature	2971.01	2980.28	24.90	6.198800e+02	2.78
CH-BIO bio02 mean diurnal air temperature range	69.32	74.00	19.84	3.937500e+02	2.22
CH-BIO bio03 isothermality	647.15	665.98	54.68	2.990000e+03	6.11
CH-BIO bio04 temperature seasonality	1209.24	1160.97	177.94	3.166225e+04	19.89
CH-BIO bio05 mean daily maximum air temperature of the warmest month	3025.08	3026.36	25.64	6.572200e+02	2.87
CH-BIO bio06 mean daily minimum air temperature of the coldest month	2919.74	2919.67	31.04	9.637200e+02	3.47
CH-BIO bio07 annual range of air temperature	105.34	107.51	24.65	6.074400e+02	2.76
CH-BIO bio08 mean daily mean air temperatures of the wettest quarter	2976.93	2982.02	24.61	6.056200e+02	2.75
CH-BIO bio09 mean daily mean air temperatures of the driest quarter	2959.57	2971.07	27.74	7.694300e+02	3.10
CH-BIO bio10 mean daily mean air temperatures of the warmest quarter	2985.37	2996.55	25.19	6.347700e+02	2.82
CH-BIO bio11 mean daily mean air temperatures of the coldest quarter	2954.89	2961.28	25.46	6.484100e+02	2.85
CH-BIO bio12 annual precipitation amount	15999.69	16272.24	4093.12	1.675366e+07	457.63
CH-BIO bio13 precipitation amount of the wettest month	2202.99	2200.08	529.53	2.804042e+05	59.20
CH-BIO bio14 precipitation amount of the driest month	664.92	698.84	264.17	6.978463e+04	29.53
CH-BIO bio15 precipitation seasonality	366.84	349.88	87.41	7.640240e+03	9.77
CH-BIO bio16 mean monthly precipitation amount of the wettest quarter	5380.32	5643.61	1202.94	1.447070e+06	134.49
CH-BIO bio17 mean monthly precipitation amount of the driest quarter	2317.33	2414.63	888.53	7.894918e+05	99.34
CH-BIO bio18 mean monthly precipitation amount of the warmest quarter	4238.44	4086.74	1250.31	1.563266e+06	139.79
CH-BIO bio19 mean monthly precipitation amount of the coldest quarter	2563.21	2485.84	1106.72	1.224838e+06	123.74
ESA Barren / sparse vegetation	0.46	0.13	0.86	7.300000e-01	0.10
ESA Built-up	4.29	1.07	9.29	8.639000e+01	1.04
ESA Cropland	2.22	0.29	6.09	3.712000e+01	0.68
ESA Grassland	20.40	16.78	14.73	2.169100e+02	1.65
ESA Herbaceous wetland	0.32	0.01	1.54	2.360000e+00	0.17
ESA Mangroves	0.35	0.00	1.60	2.560000e+00	0.18
ESA Open water	11.23	0.08	21.25	4.515900e+02	2.38
ESA Shrubland	3.65	0.14	9.65	9.320000e+01	1.08
ESA Trees	57.08	59.29	24.20	5.855200e+02	2.71
G90 Compound Topographic Index	-0.50	-0.69	0.95	9.000000e-01	0.11
G90 Roughness	35.03	37.39	24.85	6.176500e+02	2.78
G90 Slope	7.13	7.66	5.05	2.546000e+01	0.56
G90 Stream Power Index	0.20	0.11	0.31	1.000000e-01	0.03
G90 Terrain Ruggedness Index	10.95	11.70	7.70	5.926000e+01	0.86
G90 Topographic Position Index	0.01	0.00	0.05	0.000000e+00	0.01
G90 Vector Ruggedness Measure	0.00	0.00	0.00	0.000000e+00	0.00
G90-GEOM flat	22.10	8.38	28.98	8.396000e+02	3.24
G90-GEOM footslope	3.58	3.34	2.84	8.040000e+00	0.32
G90-GEOM hollow	11.47	13.35	6.29	3.952000e+01	0.70
G90-GEOM peak	0.93	0.90	0.90	8.200000e-01	0.10
G90-GEOM pit	0.46	0.35	0.76	5.800000e-01	0.08
G90-GEOM ridge	8.69	10.21	4.60	2.114000e+01	0.51
G90-GEOM shoulder	3.11	1.64	3.30	1.091000e+01	0.37
G90-GEOM slope	27.47	31.33	11.68	1.365100e+02	1.31
G90-GEOM spur	12.92	14.84	6.87	4.714000e+01	0.77
G90-GEOM valley	9.27	10.31	4.73	2.236000e+01	0.53
GFC-LOSS year 2001	3.26	2.58	3.89	1.513000e+01	0.43
GFC-LOSS year 2002	2.80	2.43	1.91	3.640000e+00	0.21
GFC-LOSS year 2003	2.72	1.94	2.42	5.860000e+00	0.27
GFC-LOSS year 2004	5.29	4.87	3.46	1.199000e+01	0.39
GFC-LOSS year 2005	5.55	4.33	9.59	9.189000e+01	1.07
GFC-LOSS year 2006	3.57	3.02	2.56	6.550000e+00	0.29
GFC-LOSS year 2007	4.49	4.22	2.19	4.820000e+00	0.25
GFC-LOSS year 2008	6.19	5.40	4.28	1.834000e+01	0.48
GFC-LOSS year 2009	3.54	3.00	2.16	4.670000e+00	0.24
GFC-LOSS year 2010	3.29	3.00	1.93	3.740000e+00	0.22
GFC-LOSS year 2011	3.38	3.20	2.41	5.810000e+00	0.27
GFC-LOSS year 2012	4.81	4.19	3.37	1.139000e+01	0.38
GFC-LOSS year 2013	3.47	2.36	3.37	1.134000e+01	0.38
GFC-LOSS year 2014	4.32	3.94	2.46	6.040000e+00	0.27
GFC-LOSS year 2015	5.13	3.97	4.94	2.442000e+01	0.55
GFC-LOSS year 2016	7.05	5.91	5.78	3.341000e+01	0.65
GFC-LOSS year 2017	8.95	8.00	5.65	3.197000e+01	0.63
GFC-LOSS year 2018	5.29	4.79	3.36	1.128000e+01	0.38
GFC-LOSS year 2019	5.77	4.21	5.33	2.845000e+01	0.60
GFC-LOSS year 2020	6.66	5.41	7.30	5.335000e+01	0.82
GFC-LOSS year 2021	4.47	3.97	3.02	9.120000e+00	0.34
GFC-PTC YEAR 2000 mean	44.01	41.27	21.84	4.767700e+02	2.44
GHH coefficient_of_variation_1km	501.19	456.87	195.06	3.804906e+04	21.81
GHH contrast_1km	124990.66	109319.27	74687.78	5.578265e+09	8350.35
GHH correlation_1km	2413.59	2427.34	349.10	1.218717e+05	39.03
GHH dissimilarity_1km	24183.19	23394.15	6095.07	3.714988e+07	681.45
GHH entropy_1km	26388.74	26611.34	927.17	8.596445e+05	103.66
GHH homogeneity_1km	3688.18	3672.52	500.79	2.507883e+05	55.99
GHH maximum_1km	1379.89	1347.76	177.41	3.147321e+04	19.83
GHH mean_1km	5896.31	6109.77	796.82	6.349242e+05	89.09
GHH pielou_1km	9260.40	9270.31	102.56	1.051947e+04	11.47
GHH range_1km	968.51	918.19	251.84	6.342362e+04	28.16
GHH shannon_1km	18763.69	18853.02	1222.78	1.495199e+06	136.71
GHH simpson_1km	8173.61	8197.78	233.92	5.471787e+04	26.15
GHH standard_deviation_1km	276.20	262.91	74.73	5.584300e+03	8.35
GHH uniformity_1km	838.42	811.37	106.53	1.134927e+04	11.91
GHH variance_1km	93327.95	75595.72	59352.78	3.522752e+09	6635.84
GP-CONSUNadj YEAR 2020 sum	92662.69	18673.63	259173.75	6.717103e+10	28976.51
GSL Cliff	0.01	0.00	0.05	0.000000e+00	0.01
GSL Lower slope	0.92	0.25	1.66	2.740000e+00	0.19
GSL Lower slope (cool)	0.00	0.00	0.00	0.000000e+00	0.00
GSL Lower slope (flat)	13.31	7.78	14.85	2.206600e+02	1.66
GSL Lower slope (warm)	31.70	37.24	14.66	2.148900e+02	1.64
GSL Mountain/divide	0.17	0.00	0.36	1.300000e-01	0.04
GSL Peak/ridge	0.04	0.01	0.07	0.000000e+00	0.01
GSL Peak/ridge (warm)	2.56	2.80	1.96	3.830000e+00	0.22
GSL Upper slope	1.32	0.38	2.10	4.410000e+00	0.23
GSL Upper slope (cool)	0.00	0.00	0.00	0.000000e+00	0.00
GSL Upper slope (flat)	9.94	0.33	16.72	2.796900e+02	1.87
GSL Upper slope (warm)	30.55	34.55	13.77	1.895700e+02	1.54
GSL Valley	8.34	8.42	2.64	6.980000e+00	0.30
GSL Valley (narrow)	1.13	0.79	1.01	1.010000e+00	0.11
OSM-DIST mean	888.69	606.56	915.83	8.387482e+05	102.39
PSEASON-IZZO mean	38.18	38.14	10.50	1.101500e+02	1.17
TSEASON-IZZO mean	1.33	1.28	0.20	4.000000e-02	0.02
WBW-DIST mean	6400.99	5323.48	4682.81	2.192873e+07	523.55
WCL bio01 Annual mean temperature	240.56	249.48	27.11	7.351700e+02	3.03
WCL bio02 Mean diurnal range mean of monthly max temp - min temp	108.03	107.02	12.76	1.627400e+02	1.43
WCL bio03 Isothermality bio02 div/bio07	73.58	73.66	2.31	5.320000e+00	0.26
WCL bio04 Temperature seasonality Standard deviation times 100	1281.31	1246.44	170.23	2.897978e+04	19.03
WCL bio05 Max temperature of warmest month	311.16	319.27	24.01	5.765000e+02	2.68
WCL bio06 Min temperature of coldest month	165.40	175.46	33.45	1.119100e+03	3.74
WCL bio07 Temperature annual range bio05-bio06	145.76	146.80	15.70	2.463500e+02	1.75
WCL bio08 Mean temperature of wettest quarter	246.33	254.97	27.97	7.821100e+02	3.13
WCL bio09 Mean temperature of driest quarter	227.43	237.60	28.94	8.376500e+02	3.24
WCL bio10 Mean temperature of warmest quarter	254.78	264.53	27.27	7.438500e+02	3.05
WCL bio11 Mean temperature of coldest quarter	222.20	230.52	27.60	7.615800e+02	3.09
WCL bio12 Annual precipitation	1412.85	1355.82	304.31	9.260508e+04	34.02
WCL bio13 Precipitation of wettest month	197.91	198.03	37.96	1.440670e+03	4.24
WCL bio14 Precipitation of driest month	57.08	55.11	21.97	4.828600e+02	2.46
WCL bio15 Precipitation seasonality	40.05	37.77	11.36	1.290300e+02	1.27
WCL bio16 Precipitation of wettest quarter	494.47	474.53	98.31	9.664300e+03	10.99
WCL bio17 Precipitation of driest quarter	189.40	182.43	70.31	4.944130e+03	7.86
WCL bio18 Precipitation of warmest quarter	394.82	375.91	118.47	1.403519e+04	13.25
WCL bio19 Precipitation of coldest quarter	228.47	212.38	109.05	1.189258e+04	12.19
YINSOLTIME mean	4219.98	4243.07	185.33	3.434807e+04	20.72

Se muestra a continuación un diagrama de cajas que sólo incluye un grupo de variables ambientales. Son muchas variables ambientales, y si se incluyen todas, el servidor no aguantaría o el gráfico no sería legible. Es por ello que ves el comando select(matches('^ESA')), el cual selecciona las columnas que comienzan por la cadena de caracteres ‘ESA’. El Sufijo de grupo “^ESA”, que se refiere a los datos de cobertura del suelo de la Agencia Espacial Europea (ESA 2021, imágenes Sentinel 1 y Sentinel 2 a 10 m de resolucion máximo). Se deben hacer gráficos para otros grupos de variables, ver la lista de grupos posibles en la columna “Sufijo del grupo” de la Tabla 1 de este HTML. Otros grupos posibles: CH-BIO, CGL, G90, WCL, entre muchos otros.

env %>%
  select(matches('^ESA')) %>%
  pivot_longer(everything(), names_to = 'Variable', values_to = 'Valor') %>% 
  group_by(Variable) %>% 
  ggplot() +
  aes(x = Variable, y = Valor, color = Variable, fill = Variable) + 
  # geom_boxplot(lwd = 0.2) + 
  geom_violin(alpha = 0.2, width = 0.8, color = "transparent") +
  geom_jitter(alpha = 0.6, size = 2, height = 0, width = 0.1) +
  geom_boxplot(alpha = 0, width = 0.3, color = "#808080") +
  scale_fill_brewer(palette = 'Set1') +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 10)) +  # Aplica str_wrap
  theme_bw() +
  theme(legend.position="none", 
        axis.text.x = element_text(angle = 45, hjust = 1))

Las medias calculadas de las variables ESA Trees, ESA Shrubland, ESA Grassland, ESA Cropland, ESA Built-up, ESA Barren / sparse vegetation, ESA Open water, ESA Herbaceous wetland, ESA Mangroves, CGL Closed forest, evergreen needle leaf, CGL Closed forest, evergreen broad leaf, CGL Closed forest, deciduous broad leaf, CGL Closed forest, mixed, CGL Closed forest, not matching any of the other definitions, CGL Open forest, evergreen needle leaf, CGL Open forest, evergreen broad leaf, CGL Open forest, deciduous broad leaf, CGL Open forest, mixed, CGL Open forest, not matching any of the other definitions, CGL Shrubs, CGL Oceans, seas, CGL Herbaceous vegetation, CGL Cultivated and managed vegetation / agriculture, CGL Urban / built up, CGL Bare / sparse vegetation, CGL Permanent water bodies, CGL Herbaceous wetland, GSL Peak/ridge (warm), GSL Peak/ridge, GSL Mountain/divide, GSL Cliff, GSL Upper slope (warm), GSL Upper slope, GSL Upper slope (cool), GSL Upper slope (flat), GSL Lower slope (warm), GSL Lower slope, GSL Lower slope (cool), GSL Lower slope (flat), GSL Valley, GSL Valley (narrow), GHH coefficient_of_variation_1km, GHH contrast_1km, GHH correlation_1km, GHH dissimilarity_1km, GHH entropy_1km, GHH homogeneity_1km, GHH maximum_1km, GHH mean_1km, GHH pielou_1km, GHH range_1km, GHH shannon_1km, GHH simpson_1km, GHH standard_deviation_1km, GHH uniformity_1km, GHH variance_1km, WCL bio01 Annual mean temperature, WCL bio02 Mean diurnal range mean of monthly max temp - min temp, WCL bio03 Isothermality bio02 div/bio07, WCL bio04 Temperature seasonality Standard deviation times 100, WCL bio05 Max temperature of warmest month, WCL bio06 Min temperature of coldest month, WCL bio07 Temperature annual range bio05-bio06, WCL bio08 Mean temperature of wettest quarter, WCL bio09 Mean temperature of driest quarter, WCL bio10 Mean temperature of warmest quarter, WCL bio11 Mean temperature of coldest quarter, WCL bio12 Annual precipitation, WCL bio13 Precipitation of wettest month, WCL bio14 Precipitation of driest month, WCL bio15 Precipitation seasonality, WCL bio16 Precipitation of wettest quarter, WCL bio17 Precipitation of driest quarter, WCL bio18 Precipitation of warmest quarter, WCL bio19 Precipitation of coldest quarter, CH-BIO bio01 mean annual air temperature, CH-BIO bio02 mean diurnal air temperature range, CH-BIO bio03 isothermality, CH-BIO bio04 temperature seasonality, CH-BIO bio05 mean daily maximum air temperature of the warmest month, CH-BIO bio06 mean daily minimum air temperature of the coldest month, CH-BIO bio07 annual range of air temperature, CH-BIO bio08 mean daily mean air temperatures of the wettest quarter, CH-BIO bio09 mean daily mean air temperatures of the driest quarter, CH-BIO bio10 mean daily mean air temperatures of the warmest quarter, CH-BIO bio11 mean daily mean air temperatures of the coldest quarter, CH-BIO bio12 annual precipitation amount, CH-BIO bio13 precipitation amount of the wettest month, CH-BIO bio14 precipitation amount of the driest month, CH-BIO bio15 precipitation seasonality, CH-BIO bio16 mean monthly precipitation amount of the wettest quarter, CH-BIO bio17 mean monthly precipitation amount of the driest quarter, CH-BIO bio18 mean monthly precipitation amount of the warmest quarter, CH-BIO bio19 mean monthly precipitation amount of the coldest quarter, G90 Compound Topographic Index, G90 Roughness, G90 Slope, G90 Stream Power Index, G90 Terrain Ruggedness Index, G90 Topographic Position Index, G90 Vector Ruggedness Measure, G90-GEOM flat, G90-GEOM pit, G90-GEOM peak, G90-GEOM ridge, G90-GEOM shoulder, G90-GEOM spur, G90-GEOM slope, G90-GEOM hollow, G90-GEOM footslope, G90-GEOM valley, CGIAR-ELE mean, GFC-PTC YEAR 2000 mean, GFC-LOSS year 2001, GFC-LOSS year 2002, GFC-LOSS year 2003, GFC-LOSS year 2004, GFC-LOSS year 2005, GFC-LOSS year 2006, GFC-LOSS year 2007, GFC-LOSS year 2008, GFC-LOSS year 2009, GFC-LOSS year 2010, GFC-LOSS year 2011, GFC-LOSS year 2012, GFC-LOSS year 2013, GFC-LOSS year 2014, GFC-LOSS year 2015, GFC-LOSS year 2016, GFC-LOSS year 2017, GFC-LOSS year 2018, GFC-LOSS year 2019, GFC-LOSS year 2020, GFC-LOSS year 2021, OSM-DIST mean, GP-CONSUNadj YEAR 2020 sum, YINSOLTIME mean, WBW-DIST mean, TSEASON-IZZO mean y PSEASON-IZZO mean son, respectivamente, las siguientes: 57.08, 3.65, 20.4, 2.22, 4.29, 0.46, 11.23, 0.32, 0.35, 0.01, 21.79, 0.16, 2.36, 1.27, 0, 7.4, 0.17, 0, 28.68, 7.47, 10.57, 10.53, 3.52, 4.69, 0.02, 0.56, 0.78, 2.56, 0.04, 0.17, 0.01, 30.55, 1.32, 0, 9.94, 31.7, 0.92, 0, 13.31, 8.34, 1.13, 501.19, 124990.66, 2413.59, 24183.19, 26388.74, 3688.18, 1379.89, 5896.31, 9260.4, 968.51, 18763.69, 8173.61, 276.2, 838.42, 93327.95, 240.56, 108.03, 73.58, 1281.31, 311.16, 165.4, 145.76, 246.33, 227.43, 254.78, 222.2, 1412.85, 197.91, 57.08, 40.05, 494.47, 189.4, 394.82, 228.47, 2971.01, 69.32, 647.15, 1209.24, 3025.08, 2919.74, 105.34, 2976.93, 2959.57, 2985.37, 2954.89, 15999.69, 2202.99, 664.92, 366.84, 5380.32, 2317.33, 4238.44, 2563.21, -0.5, 35.03, 7.13, 0.2, 10.95, 0.01, 0, 22.1, 0.46, 0.93, 8.69, 3.11, 12.92, 27.47, 11.47, 3.58, 9.27, 367.65, 44.01, 3.26, 2.8, 2.72, 5.29, 5.55, 3.57, 4.49, 6.19, 3.54, 3.29, 3.38, 4.81, 3.47, 4.32, 5.13, 7.05, 8.95, 5.29, 5.77, 6.66, 4.47, 888.69, 92662.69, 4219.98, 6400.99, 1.33 y 38.18. La variable que con la media más alta fue GHH contrast_1km (1.2499066^{5}), y la más baja la obtuvo la variable G90 Compound Topographic Index (-0.5). Por otra parte, la mitad de los sitios midieron menos de 59.29, 0.14, 16.78, 0.29, 1.07, 0.13, 0.08, 0.01, 0, 0, 17.22, 0, 0.02, 1.01, 0, 6.48, 0, 0, 28.55, 2.37, 0, 7.19, 0.58, 1.04, 0, 0, 0.18, 2.8, 0.01, 0, 0, 34.55, 0.38, 0, 0.33, 37.24, 0.25, 0, 7.78, 8.42, 0.79, 456.87, 109319.27, 2427.34, 23394.15, 26611.34, 3672.52, 1347.76, 6109.77, 9270.31, 918.19, 18853.02, 8197.78, 262.91, 811.37, 75595.72, 249.48, 107.02, 73.66, 1246.44, 319.27, 175.46, 146.8, 254.97, 237.6, 264.53, 230.52, 1355.82, 198.03, 55.11, 37.77, 474.53, 182.43, 375.91, 212.38, 2980.28, 74, 665.98, 1160.97, 3026.36, 2919.67, 107.51, 2982.02, 2971.07, 2996.55, 2961.28, 16272.24, 2200.08, 698.84, 349.88, 5643.61, 2414.63, 4086.74, 2485.84, -0.69, 37.39, 7.66, 0.11, 11.7, 0, 0, 8.38, 0.35, 0.9, 10.21, 1.64, 14.84, 31.33, 13.35, 3.34, 10.31, 236.67, 41.27, 2.58, 2.43, 1.94, 4.87, 4.33, 3.02, 4.22, 5.4, 3, 3, 3.2, 4.19, 2.36, 3.94, 3.97, 5.91, 8, 4.79, 4.21, 5.41, 3.97, 606.56, 18673.63, 4243.07, 5323.48, 1.28 y 38.14, para cada una de las variables CGIAR-ELE mean, CGL Bare / sparse vegetation, CGL Closed forest, deciduous broad leaf, CGL Closed forest, evergreen broad leaf, CGL Closed forest, evergreen needle leaf, CGL Closed forest, mixed, CGL Closed forest, not matching any of the other definitions, CGL Cultivated and managed vegetation / agriculture, CGL Herbaceous vegetation, CGL Herbaceous wetland, CGL Oceans, seas, CGL Open forest, deciduous broad leaf, CGL Open forest, evergreen broad leaf, CGL Open forest, evergreen needle leaf, CGL Open forest, mixed, CGL Open forest, not matching any of the other definitions, CGL Permanent water bodies, CGL Shrubs, CGL Urban / built up, CH-BIO bio01 mean annual air temperature, CH-BIO bio02 mean diurnal air temperature range, CH-BIO bio03 isothermality, CH-BIO bio04 temperature seasonality, CH-BIO bio05 mean daily maximum air temperature of the warmest month, CH-BIO bio06 mean daily minimum air temperature of the coldest month, CH-BIO bio07 annual range of air temperature, CH-BIO bio08 mean daily mean air temperatures of the wettest quarter, CH-BIO bio09 mean daily mean air temperatures of the driest quarter, CH-BIO bio10 mean daily mean air temperatures of the warmest quarter, CH-BIO bio11 mean daily mean air temperatures of the coldest quarter, CH-BIO bio12 annual precipitation amount, CH-BIO bio13 precipitation amount of the wettest month, CH-BIO bio14 precipitation amount of the driest month, CH-BIO bio15 precipitation seasonality, CH-BIO bio16 mean monthly precipitation amount of the wettest quarter, CH-BIO bio17 mean monthly precipitation amount of the driest quarter, CH-BIO bio18 mean monthly precipitation amount of the warmest quarter, CH-BIO bio19 mean monthly precipitation amount of the coldest quarter, ESA Barren / sparse vegetation, ESA Built-up, ESA Cropland, ESA Grassland, ESA Herbaceous wetland, ESA Mangroves, ESA Open water, ESA Shrubland, ESA Trees, G90 Compound Topographic Index, G90 Roughness, G90 Slope, G90 Stream Power Index, G90 Terrain Ruggedness Index, G90 Topographic Position Index, G90 Vector Ruggedness Measure, G90-GEOM flat, G90-GEOM footslope, G90-GEOM hollow, G90-GEOM peak, G90-GEOM pit, G90-GEOM ridge, G90-GEOM shoulder, G90-GEOM slope, G90-GEOM spur, G90-GEOM valley, GFC-LOSS year 2001, GFC-LOSS year 2002, GFC-LOSS year 2003, GFC-LOSS year 2004, GFC-LOSS year 2005, GFC-LOSS year 2006, GFC-LOSS year 2007, GFC-LOSS year 2008, GFC-LOSS year 2009, GFC-LOSS year 2010, GFC-LOSS year 2011, GFC-LOSS year 2012, GFC-LOSS year 2013, GFC-LOSS year 2014, GFC-LOSS year 2015, GFC-LOSS year 2016, GFC-LOSS year 2017, GFC-LOSS year 2018, GFC-LOSS year 2019, GFC-LOSS year 2020, GFC-LOSS year 2021, GFC-PTC YEAR 2000 mean, GHH coefficient_of_variation_1km, GHH contrast_1km, GHH correlation_1km, GHH dissimilarity_1km, GHH entropy_1km, GHH homogeneity_1km, GHH maximum_1km, GHH mean_1km, GHH pielou_1km, GHH range_1km, GHH shannon_1km, GHH simpson_1km, GHH standard_deviation_1km, GHH uniformity_1km, GHH variance_1km, GP-CONSUNadj YEAR 2020 sum, GSL Cliff, GSL Lower slope, GSL Lower slope (cool), GSL Lower slope (flat), GSL Lower slope (warm), GSL Mountain/divide, GSL Peak/ridge, GSL Peak/ridge (warm), GSL Upper slope, GSL Upper slope (cool), GSL Upper slope (flat), GSL Upper slope (warm), GSL Valley, GSL Valley (narrow), OSM-DIST mean, PSEASON-IZZO mean, TSEASON-IZZO mean, WBW-DIST mean, WCL bio01 Annual mean temperature, WCL bio02 Mean diurnal range mean of monthly max temp - min temp, WCL bio03 Isothermality bio02 div/bio07, WCL bio04 Temperature seasonality Standard deviation times 100, WCL bio05 Max temperature of warmest month, WCL bio06 Min temperature of coldest month, WCL bio07 Temperature annual range bio05-bio06, WCL bio08 Mean temperature of wettest quarter, WCL bio09 Mean temperature of driest quarter, WCL bio10 Mean temperature of warmest quarter, WCL bio11 Mean temperature of coldest quarter, WCL bio12 Annual precipitation, WCL bio13 Precipitation of wettest month, WCL bio14 Precipitation of driest month, WCL bio15 Precipitation seasonality, WCL bio16 Precipitation of wettest quarter, WCL bio17 Precipitation of driest quarter, WCL bio18 Precipitation of warmest quarter, WCL bio19 Precipitation of coldest quarter y YINSOLTIME mean, respectivamente. Finlamente, la variable con mayor dispersión fue GP-CONSUNadj YEAR 2020 sum y la de menor dispersión fue CGL Open forest, evergreen needle leaf.

Una verificación importante que debe realizarse es si las matrices de comunidad y ambiental tienen el mismo numero de filas y si las filas se encuentran en el mismo orden (e.g. consistencia entre matrices, donde cada fila en la matriz de comunidad se refiere al mismo sitio en la ambiental, y viceversa). Esto se puede comprobar por medio de los nombres de columnas y, en este caso, tras realizar la correspondiente comprobación, esta condición se cumple, por lo que podemos continuar adelante con los siguientes análisis

A continuación, realizaré análisis de agrupamiento, ordenación y diversidad, basándome en las indicaciones de Borcard, Gillet, y Legendre (2018), reaprovechando el código contenido en (jose_ramon_martinez_batlle_2020_4402362?).

2 Análisis de agrupamiento

A continuación, el análisis de agrupamiento propiamente. La parte más importante es generar un árbol, a partir de una matriz de distancias, que haga sentido desde el punto de vista de la comunidad y la distribución de las especies. Primero cargaré paquetes específicos de esta técnica y generaré la matriz de distancias.

mc_d <- vegdist(mc_t, "euc")

2.1 Generación de árboles

A continuación, generaré árboles usando distintos métodos. Explico detalladamente estas técnicas en el repo, y en los vídeos (13 a 16) de la lista mencionada arriba “Ecología Numérica con R” de mi canal.

lista_cl <- list(
        cl_single = hclust(mc_d, method = 'single'),
        cl_complete = hclust(mc_d, method = 'complete'),
        cl_upgma = hclust(mc_d, method = 'average'),
        cl_ward = hclust(mc_d, method = 'ward.D2')
)
par(mfrow = c(2,2))
invisible(map(names(lista_cl), function(x) plot(lista_cl[[x]], main = paste0(x, '\n(árbol de evaluación)'), hang = -1)))

par(mfrow = c(1,1))

A continuación, calcularé la distancia y la correlación cofenéticas; esta última, la correlación cofenética,se utiliza como criterio flexible para elegir el método de agrupamiento idóneo, pero no debe usarse de manera estricta. Se supone que el método con la mayor correlación cofenética explica mejor el agrupamiento de la comunidad. Si quieres comprender mejor esta técnica, consulta el vídeo que te referí en el párrafo anterior, así como los libros de referencia. Normalmente, el método UPGMA obtiene la mayor correlación cofenética, pero esto se debe a que su procedimiento de obtención maximiza precisamente dicha métrica. No es recomendable conservar un único método de agrupamiento, normalmente es bueno usar al menos dos. Ward es muchas veces recomendado como método de contraste, por basarse en procedimientos de cálculo muy distintos a los de UPGMA.

map_df(lista_cl, function(x) {
        coph_d <- cophenetic(x)
        corr <- cor(mc_d, coph_d)
        return(corr)
}) %>% t() %>% as.data.frame() %>%
  rownames_to_column %>%
  mutate(rowname = gsub('cl_', '', rowname)) %>% 
  setNames(c('Método de agrupamiento', 'Correlación cofenética')) %>%
  estilo_kable()

Table 2.1:
Método de agrupamiento	Correlación cofenética
single	0.50
complete	0.59
upgma	0.66
ward	0.50

2.2 Anchura de siluetas

Ahora, calcularé las anchuras de silueta, una métrica que ayuda a determinar en cuántos grupos se organiza la comunidad; las anchuras de silueta no deben usarse como método estricto, y sólo debe usarse de forma flexible para informarnos sobre el número máximo de grupos posibles. Considera las siguientes reglas:

El número ideal es 3 grupos, de 4 a 5 grupos es aceptable, 6 o más grupos se considera difícil de interpretar, o es un resultado poco útil; 1 grupo es un resultado sin sentido.
Si obtienes distintos grupos, pero uno o varios están compuestos por un único sitio, observa qué ocurre en ese sitio, pues es probable que contenga especie raras sólo presentes en él. En este caso, es recomendable explorar dos alternativas para evitar el grupo formado por un único sitio: ver qué ocurre usando distintos métodos o elegir cortar el árbol en un número de grupos menor.

2.2.1 Anchuras de siluetas para método UPGMA

# UPGMA
anch_sil_upgma <- calcular_anchuras_siluetas(
        mc_orig = mc, 
        distancias = mc_d, 
        cluster = lista_cl$cl_upgma)
u_dend_reord <- reorder.hclust(lista_cl$cl_upgma, mc_d)
plot(u_dend_reord, hang = -1, main = 'Método UPGMA\n(árbol de evaluación)')
rect.hclust(
        tree = u_dend_reord,
        k = anch_sil_upgma$n_grupos_optimo)

resultado_evaluacion_upgma <- evaluar_arbol(u_dend_reord, anch_sil_upgma$n_grupos_optimo)

Tras cortar el árbol, la evaluación practicada concluyó lo siguiente: “Árbol útil para análisis posteriores, siempre que se corte en 2 grupos”

2.2.2 Anchuras de siluetas para método Ward

# Ward
anch_sil_ward <- calcular_anchuras_siluetas(
        mc_orig = mc, 
        distancias = mc_d, 
        cluster = lista_cl$cl_ward)
w_dend_reord <- reorder.hclust(lista_cl$cl_ward, mc_d)
plot(w_dend_reord, hang = -1, main = 'Método Ward\n(árbol de evaluación)')
rect.hclust(
        tree = w_dend_reord,
        k = anch_sil_ward$n_grupos_optimo)

resultado_evaluacion_ward <- evaluar_arbol(w_dend_reord, anch_sil_ward$n_grupos_optimo)

Tras cortar el árbol, la evaluación practicada concluyó lo siguiente: “Árbol útil para análisis posteriores, siempre que se corte en 14 grupos”.

2.3 Remuestreo por bootstrap multiescalar

Una forma alterna de evaluar árboles consiste en usar el remuestreo por bootstrap multiescalar. No me interesa que profundices en ella, sólo presentártela como técnica probabilística para evaluar árboles generados por métodos determinísticos. La técnica es documentada en Borcard, Gillet, y Legendre (2018), de la cual puedes un resumen en este cuaderno y en este vídeo (minuto 51:33). El remuestreo por bootstrap multiescalar valida la robustez de los análisis de agrupamiento tomando múltiples muestras aleatorias de los datos en diferentes tamaños. Este proceso determina qué grupos son consistentemente identificados como clústeres, generando valores de probabilidad aproximadamente insesgados (AU) que son considerados más fiables que las probabilidades de bootstrap tradicionales (BP). Esta técnica ayuda a identificar y confirmar patrones robustos en los datos.

Lo aplicaré primero al árbol generado por el método UPGMA.

# UPGMA
# if(interactive()) dev.new()
cl_pvclust_upgma <-
        pvclust(t(mc_t),
                method.hclust = "average",
                method.dist = "euc",
                iseed = 99, # Resultado reproducible
                parallel = TRUE, quiet = TRUE)
# Añadir los valores de p
plot(cl_pvclust_upgma, hang = -1, main = 'Método UPGMA bootstrap\n(árbol de evaluación)')
# Añadir rectángulos a los grupos significativos
lines(cl_pvclust_upgma)
pvrect(cl_pvclust_upgma, alpha = 0.90, border = 4)

Lo aplicaré también al árbol generado por el método Ward.

# Ward
# if(interactive()) dev.new()
cl_pvclust_ward <-
        pvclust(t(mc_t),
                method.hclust = "ward.D2",
                method.dist = "euc",
                iseed = 99, # Resultado reproducible
                parallel = TRUE, quiet = TRUE)
# Añadir los valores de p
plot(cl_pvclust_ward, hang = -1, main = 'Método Ward bootstrap\n(árbol de evaluación)')
# Añadir rectángulos a los grupos significativos
lines(cl_pvclust_ward)
pvrect(cl_pvclust_ward, alpha = 0.91, border = 4)

2.4 Conclusión sobre selección de método de agrupamiento y número de grupos

Basado en lo anterior, elegiré un método de agrupamiento y un número de grupos, y lo exportaré a un archivo que posteriormente podré reaprovechar. La lógica empleada para elegir método de agrupamiento y número de grupos, es la siguiente: si el árbol generado por el método UPGMA no es recomendable (por tener grupos formados 2 o menos elementos), pero Ward sí, se usar el árbol generado por el método Ward y el número de grupos idóneo sugerido por la anchura de silueta. Si UPGMA es recomendable pero Ward no lo es, se usar el árbol generado por el método UPGMA, cortado en el número de grupos sugerido por la anchura de siluetas. Si ambos métodos son recomendables y sugieren el mismo número de grupos, se opta por el arbol generado por el método Ward. Si ambos métodos son recomendables pero sugieren un número diferente de grupos, se elige el método que sugiere menos grupos. Finalmente, si ambos métodos, UPGMA y Ward, resultan ser poco idóneos porque generan grupos muy pequeños (dos o menos elementos), se opta, como último recurso, por elegir el árbol generado por el método Ward cortado en 3 grupos.

grupos_seleccionados <- seleccionar_y_cortar_arbol(
  arbol_upgma = lista_cl$cl_upgma, arbol_ward = lista_cl$cl_ward,
  resultado_evaluacion_upgma = resultado_evaluacion_upgma,
  resultado_evaluacion_ward = resultado_evaluacion_ward)
saveRDS(grupos_seleccionados$resultado, 'grupos_seleccionados-Acanthaceae.RDS')

El árbol generado por el método UPGMA produce un número menor de grupos que el generado por el método Ward. Usamos el árbol generado por el método UPGMA cortado en 2 grupos . El árbol resultante se muestra a continuación:

# Convierte el hclust en dendrograma
dend <- as.dendrogram(grupos_seleccionados$arbol)

# Corta y colorea el dendrograma en k grupos
dend_colored <- color_branches(dend, k=grupos_seleccionados$k)

# Etiqueta los grupos
labels_colors <- labels_colors(dend_colored)
labels(dend_colored) <- paste0(labels(dend_colored), " (",
                               grupos_seleccionados$resultado[grupos_seleccionados$arbol$order],
                               ")")

# Grafica el dendrograma
# par(mar = c(3, 4, 4, 2) + 0.1) # Ajusta los márgenes
plot(
  dend_colored,
  main=paste(
    'Árbol seleccionado\nMétodo',
    grupos_seleccionados$metodo,
    'cortado en',
    grupos_seleccionados$k, 'grupos'),
  xlab = 'Sitios (grupo de pertenencia)')

2.5 Grupos (clústers), variables ambientales

Apliquemos el análisis de agrupamiento a la matriz ambiental. La clave en este punto es que, si la matriz ambiental presenta patrones parecidos a los de la matriz de comunidad, significa que el agrupamiento utilizado hace sentido entre ambos conjuntos de datos (comunidad y hábitat) de forma consistente. Si ambos conjuntos de datos son consistentes, significa que existe algún grado de asociación, aunque sea sólo una mera asociación estadística.

Agrupar los sitios de muestreo de la matriz ambiental según los grupos previamente definidos.

env_grupos <- env %>%
    rownames_to_column('sitios_de_muestreo') %>% 
    mutate(grupos = as.factor(grupos_seleccionados$resultado)) %>%
    pivot_longer(-c(grupos, sitios_de_muestreo), names_to = "variable", values_to = "valor")

Evaluar efectos entre los grupos (“diferencias significativas”). Se utilizan las pruebas estadísticas ANOVA (evalúa homongeneidad de medias) y Kruskal-Wallis (evalúa homogeneidad de medianas). Las tablas están ordenadas en orden ascendente por la columna p_valor_a, que son los p-valores de la prueba ANOVA.

env_grupos_ak <- env_grupos %>%
  group_by(variable) %>%
  summarise(
    p_valor_a = tryCatch(oneway.test(valor ~ grupos)$p.value, error = function(e) NA),
    p_valor_k = tryCatch(kruskal.test(valor ~ grupos)$p.value, error = function(e) NA)
    ) %>%
  arrange(p_valor_a)
env_grupos_ak %>% estilo_kable(alinear = 'crr')

Table 2.2:
variable	p_valor_a	p_valor_k
GFC-LOSS year 2006	0.00	0.03
GFC-LOSS year 2007	0.00	0.02
ESA Shrubland	0.03	0.34
GFC-LOSS year 2004	0.03	0.10
CGL Open forest, not matching any of the other definitions	0.04	0.09
GFC-LOSS year 2012	0.04	0.04
GFC-LOSS year 2008	0.05	0.07
GFC-LOSS year 2002	0.08	0.30
GFC-LOSS year 2018	0.11	0.26
G90-GEOM shoulder	0.12	0.26
GFC-LOSS year 2016	0.13	0.07
G90-GEOM footslope	0.13	0.15
GFC-LOSS year 2009	0.14	0.06
ESA Barren / sparse vegetation	0.14	0.07
GFC-LOSS year 2015	0.15	0.08
GFC-LOSS year 2001	0.19	0.30
WCL bio18 Precipitation of warmest quarter	0.19	0.15
TSEASON-IZZO mean	0.21	0.33
GFC-LOSS year 2021	0.22	0.21
WCL bio07 Temperature annual range bio05-bio06	0.25	0.23
CGL Urban / built up	0.26	0.55
WCL bio12 Annual precipitation	0.27	0.24
OSM-DIST mean	0.27	0.15
CGL Herbaceous wetland	0.28	0.92
GSL Valley	0.29	0.40
WCL bio16 Precipitation of wettest quarter	0.30	0.27
GHH coefficient_of_variation_1km	0.31	0.58
ESA Mangroves	0.31	0.20
WCL bio05 Max temperature of warmest month	0.31	0.20
WCL bio04 Temperature seasonality Standard deviation times 100	0.32	0.33
GHH mean_1km	0.33	0.27
ESA Built-up	0.34	0.51
CGL Closed forest, evergreen broad leaf	0.35	0.29
GFC-LOSS year 2019	0.36	0.78
WCL bio14 Precipitation of driest month	0.36	0.36
GSL Mountain/divide	0.37	0.44
ESA Herbaceous wetland	0.37	0.93
WCL bio02 Mean diurnal range mean of monthly max temp - min temp	0.39	0.41
GFC-LOSS year 2011	0.39	0.25
CH-BIO bio15 precipitation seasonality	0.41	0.31
GHH contrast_1km	0.41	0.98
CGL Closed forest, evergreen needle leaf	0.41	0.20
CGL Open forest, deciduous broad leaf	0.42	0.72
G90-GEOM pit	0.42	0.88
WCL bio17 Precipitation of driest quarter	0.43	0.40
GSL Lower slope (warm)	0.43	0.33
CH-BIO bio03 isothermality	0.44	0.87
CGL Closed forest, deciduous broad leaf	0.45	0.29
GHH variance_1km	0.45	0.90
G90-GEOM slope	0.46	0.68
CH-BIO bio18 mean monthly precipitation amount of the warmest quarter	0.46	0.52
CGL Permanent water bodies	0.47	0.97
CGL Shrubs	0.47	0.86
GHH dissimilarity_1km	0.48	0.91
G90-GEOM peak	0.50	0.70
WCL bio03 Isothermality bio02 div/bio07	0.51	0.50
GP-CONSUNadj YEAR 2020 sum	0.52	0.74
WCL bio10 Mean temperature of warmest quarter	0.53	0.70
CH-BIO bio05 mean daily maximum air temperature of the warmest month	0.53	0.86
GHH standard_deviation_1km	0.54	0.92
G90-GEOM flat	0.54	0.80
GHH range_1km	0.54	0.96
G90 Topographic Position Index	0.54	0.36
WCL bio19 Precipitation of coldest quarter	0.56	0.55
GSL Peak/ridge	0.57	0.21
WCL bio01 Annual mean temperature	0.58	0.91
GSL Upper slope	0.58	0.34
CH-BIO bio19 mean monthly precipitation amount of the coldest quarter	0.58	0.56
GHH pielou_1km	0.60	0.65
WCL bio09 Mean temperature of driest quarter	0.61	0.81
CH-BIO bio10 mean daily mean air temperatures of the warmest quarter	0.61	0.92
CH-BIO bio17 mean monthly precipitation amount of the driest quarter	0.61	0.57
WCL bio08 Mean temperature of wettest quarter	0.61	0.91
PSEASON-IZZO mean	0.62	0.72
WCL bio11 Mean temperature of coldest quarter	0.63	0.93
GSL Lower slope (flat)	0.63	0.93
CH-BIO bio12 annual precipitation amount	0.63	0.59
CH-BIO bio08 mean daily mean air temperatures of the wettest quarter	0.63	0.86
CH-BIO bio01 mean annual air temperature	0.64	0.81
G90-GEOM hollow	0.64	0.78
WCL bio13 Precipitation of wettest month	0.66	0.64
CH-BIO bio04 temperature seasonality	0.66	0.83
CH-BIO bio13 precipitation amount of the wettest month	0.66	0.77
GSL Peak/ridge (warm)	0.66	0.78
CH-BIO bio11 mean daily mean air temperatures of the coldest quarter	0.66	0.83
ESA Cropland	0.66	0.48
CH-BIO bio14 precipitation amount of the driest month	0.66	0.69
CH-BIO bio09 mean daily mean air temperatures of the driest quarter	0.66	0.58
GFC-LOSS year 2017	0.67	0.99
CGIAR-ELE mean	0.68	0.93
CH-BIO bio07 annual range of air temperature	0.69	0.66
GFC-LOSS year 2005	0.69	0.62
GSL Upper slope (flat)	0.70	0.97
GHH homogeneity_1km	0.70	0.78
WBW-DIST mean	0.70	0.74
WCL bio15 Precipitation seasonality	0.71	0.48
GFC-LOSS year 2014	0.72	0.98
GHH correlation_1km	0.72	0.52
G90-GEOM spur	0.72	0.93
G90 Compound Topographic Index	0.72	0.88
GFC-PTC YEAR 2000 mean	0.73	0.78
WCL bio06 Min temperature of coldest month	0.73	0.91
GHH shannon_1km	0.73	0.84
GFC-LOSS year 2010	0.73	0.58
CH-BIO bio06 mean daily minimum air temperature of the coldest month	0.73	0.73
YINSOLTIME mean	0.74	0.73
GHH maximum_1km	0.75	0.54
CGL Closed forest, mixed	0.76	0.70
CGL Herbaceous vegetation	0.76	0.59
CGL Open forest, evergreen broad leaf	0.77	0.61
GHH uniformity_1km	0.77	0.55
CGL Cultivated and managed vegetation / agriculture	0.77	0.98
G90 Slope	0.77	0.73
ESA Grassland	0.78	0.92
GHH entropy_1km	0.78	0.70
GHH simpson_1km	0.79	0.88
G90 Roughness	0.81	0.70
GFC-LOSS year 2013	0.81	0.72
CH-BIO bio02 mean diurnal air temperature range	0.82	0.65
CGL Closed forest, not matching any of the other definitions	0.82	0.61
GSL Lower slope	0.82	0.45
G90 Terrain Ruggedness Index	0.83	0.71
CH-BIO bio16 mean monthly precipitation amount of the wettest quarter	0.84	0.72
GSL Upper slope (warm)	0.85	0.86
CGL Oceans, seas	0.85	0.49
G90 Vector Ruggedness Measure	0.86	0.78
ESA Trees	0.86	0.95
GFC-LOSS year 2020	0.89	0.43
ESA Open water	0.89	0.96
G90-GEOM valley	0.90	0.86
GSL Valley (narrow)	0.90	0.90
CGL Bare / sparse vegetation	0.92	0.53
GFC-LOSS year 2003	0.92	0.69
G90-GEOM ridge	0.93	0.72
G90 Stream Power Index	0.94	0.65
GSL Cliff	0.95	0.97
CGL Open forest, mixed	0.95	0.91
CGL Open forest, evergreen needle leaf	NaN	NaN
GSL Lower slope (cool)	NaN	NaN
GSL Upper slope (cool)	NaN	0.72

Explora tus resultados.

env_grupos %>% 
        group_by(variable) %>% 
        ggplot() + aes(x = grupos, y = valor, group = grupos, fill = grupos) + 
        geom_boxplot(lwd = 0.2) + 
        scale_fill_brewer(palette = 'Set1') +
        theme_bw() +
        theme(legend.position="none") +
        facet_wrap(~ variable, scales = 'free_y', ncol = 8)

El objetivo de adjuntarle, a la matriz ambiental, el vector de agrupamiento generado a partir de datos de comunidad, consiste en caracterizar ambientalmente los hábitats de los subgrupos diferenciados según su composición. Observa los resultados de las pruebas estadísticas, de los diagramas de caja, y explora tus resultados:

2.6 Especies con preferencia/fidelidad con grupos (clústers)

Análisis de preferencia/fidelidad de especies con grupos (clusters), mediante el coeficiente de correlación biserial puntual (phi).

set.seed(9999)
phi <- multipatt(
  mc,
  grupos_seleccionados$resultado,
  func = "r.g",
  max.order = 1,
  control = how(nperm = 999))
summary(phi)


 Multilevel pattern analysis
 ---------------------------

 Association function: r.g
 Significance level (alpha): 0.05

 Total number of species: 41
 Selected number of species: 4 
 Number of species associated to 1 group: 4 

 List of species associated to each combination: 

 Group A  #sps.  1 
                         stat p.value  
Ruellia simplex C.Wright 0.44   0.038 *

 Group B  #sps.  3 
                                                    stat p.value    
Ruellia coccinea (L.) Vahl                         0.542   0.001 ***
Lepidagathis alopecuroidea (Vahl) R.Br. ex Griseb. 0.490   0.002 ** 
Avicennia germinans (L.) L.                        0.448   0.007 ** 
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Tabla de especies que presentaron asociación con grupos por medio de phi, usando umbral de significancia (umbral_alfa).

tabla_phi_sign <- phi$sign
tabla_phi_sign_alfa <- tabla_phi_sign[phi$sign$p.value < umbral_alfa, ]
data.frame(
  `Nombre de especie` = rownames(tabla_phi_sign_alfa),
  `P-valor` = tabla_phi_sign_alfa$p.value,
  `Grupo de asociación` = gsub('s\\.', '', names(tabla_phi_sign_alfa)[tabla_phi_sign_alfa$index]),
  check.names = F) %>%
  arrange(`Nombre de especie`) %>% 
  estilo_kable(alinear = 'crr')

Table 2.3:
Nombre de especie	P-valor	Grupo de asociación
Avicennia germinans (L.) L.	0.01	B
Lepidagathis alopecuroidea (Vahl) R.Br. ex Griseb.	0.00	B
Ruellia coccinea (L.) Vahl	0.00	B
Ruellia simplex C.Wright	0.04	A

3 Técnicas de ordenación

Me basaré en los scripts que comienzan por to_ de este repo, los cuales explico en los vídeos de “Técnicas de ordenación” de la lista de reproducción “Ecología Numérica con R” de mi canal.

3.1 Ordenación no restringida

3.1.1 PCA aplicado a datos de comunidad transformados

pca_mc_t <- rda(mc_t)
summary(pca_mc_t)


Call:
rda(X = mc_t) 

Partitioning of variance:
              Inertia Proportion
Total          0.8599          1
Unconstrained  0.8599          1

Eigenvalues, and their contribution to the variance 

Importance of components:
                          PC1     PC2     PC3     PC4     PC5     PC6     PC7
Eigenvalue            0.09673 0.07766 0.06633 0.06040 0.05146 0.04925 0.04303
Proportion Explained  0.11250 0.09031 0.07714 0.07024 0.05984 0.05728 0.05004
Cumulative Proportion 0.11250 0.20281 0.27995 0.35019 0.41003 0.46731 0.51735
                          PC8     PC9    PC10    PC11    PC12    PC13    PC14
Eigenvalue            0.03681 0.03252 0.02862 0.02823 0.02547 0.02325 0.02151
Proportion Explained  0.04281 0.03782 0.03328 0.03282 0.02962 0.02704 0.02502
Cumulative Proportion 0.56016 0.59798 0.63126 0.66409 0.69370 0.72074 0.74576
                         PC15    PC16    PC17    PC18    PC19    PC20    PC21
Eigenvalue            0.02044 0.01976 0.01939 0.01607 0.01522 0.01403 0.01376
Proportion Explained  0.02377 0.02298 0.02255 0.01868 0.01770 0.01632 0.01600
Cumulative Proportion 0.76953 0.79251 0.81506 0.83375 0.85145 0.86777 0.88377
                         PC22    PC23     PC24     PC25     PC26     PC27
Eigenvalue            0.01233 0.01095 0.009598 0.009144 0.008859 0.006993
Proportion Explained  0.01434 0.01274 0.011162 0.010634 0.010303 0.008132
Cumulative Proportion 0.89810 0.91084 0.922000 0.932634 0.942937 0.951069
                          PC28     PC29     PC30     PC31     PC32     PC33
Eigenvalue            0.006327 0.005857 0.004961 0.004115 0.003954 0.003121
Proportion Explained  0.007358 0.006811 0.005770 0.004786 0.004598 0.003630
Cumulative Proportion 0.958427 0.965238 0.971008 0.975794 0.980392 0.984022
                          PC34     PC35     PC36     PC37     PC38     PC39
Eigenvalue            0.003020 0.002527 0.002087 0.001921 0.001473 0.001173
Proportion Explained  0.003512 0.002939 0.002428 0.002234 0.001713 0.001364
Cumulative Proportion 0.987535 0.990473 0.992901 0.995135 0.996847 0.998211
                           PC40      PC41
Eigenvalue            0.0009298 0.0006082
Proportion Explained  0.0010813 0.0007074
Cumulative Proportion 0.9992926 1.0000000

screeplot(
  pca_mc_t,
  bstick = TRUE,
  npcs = length(pca_mc_t$CA$eig)
)

# Biplot
cleanplot.pca(pca_mc_t, scaling = 1, mar.percent = 0.06, cex.char1 = 0.7)

3.1.2 Análisis de correspondencia (CA)

# Realizar el CA
mc_ca <- cca(mc)

Resumen de análisis de correspondencia.

summary(mc_ca)


Call:
cca(X = mc) 

Partitioning of scaled Chi-square:
              Inertia Proportion
Total           10.25          1
Unconstrained   10.25          1

Eigenvalues, and their contribution to the scaled Chi-square 

Importance of components:
                          CA1    CA2     CA3    CA4    CA5     CA6     CA7
Eigenvalue            0.65901 0.6270 0.58738 0.5758 0.5338 0.51698 0.48198
Proportion Explained  0.06432 0.0612 0.05733 0.0562 0.0521 0.05046 0.04704
Cumulative Proportion 0.06432 0.1255 0.18285 0.2390 0.2911 0.34161 0.38865
                          CA8     CA9    CA10    CA11    CA12    CA13    CA14
Eigenvalue            0.45788 0.42275 0.39322 0.38673 0.36635 0.35307 0.30101
Proportion Explained  0.04469 0.04126 0.03838 0.03775 0.03576 0.03446 0.02938
Cumulative Proportion 0.43334 0.47461 0.51299 0.55073 0.58649 0.62095 0.65033
                         CA15    CA16    CA17    CA18    CA19   CA20    CA21
Eigenvalue            0.28023 0.27222 0.24948 0.23912 0.22695 0.2182 0.20445
Proportion Explained  0.02735 0.02657 0.02435 0.02334 0.02215 0.0213 0.01996
Cumulative Proportion 0.67768 0.70425 0.72860 0.75194 0.77410 0.7954 0.81535
                         CA22    CA23    CA24    CA25    CA26    CA27    CA28
Eigenvalue            0.20066 0.19275 0.17797 0.16487 0.14183 0.12691 0.12430
Proportion Explained  0.01958 0.01881 0.01737 0.01609 0.01384 0.01239 0.01213
Cumulative Proportion 0.83494 0.85375 0.87112 0.88721 0.90105 0.91344 0.92557
                         CA29     CA30     CA31     CA32     CA33    CA34
Eigenvalue            0.11532 0.098651 0.093685 0.077159 0.072764 0.06536
Proportion Explained  0.01126 0.009629 0.009144 0.007531 0.007102 0.00638
Cumulative Proportion 0.93683 0.946458 0.955602 0.963134 0.970236 0.97662
                          CA35    CA36     CA37     CA38    CA39     CA40
Eigenvalue            0.056942 0.05123 0.041112 0.035016 0.03053 0.024757
Proportion Explained  0.005558 0.00500 0.004013 0.003418 0.00298 0.002416
Cumulative Proportion 0.982173 0.98717 0.991186 0.994604 0.99758 1.000000

Gráfico de sedimentación o screeplot.

# Screeplot
screeplot(mc_ca, bstick = TRUE, npcs = length(mc_ca$CA$eig))

Representación del biplot.

# Biplot
plot(mc_ca,
     scaling = 1,
     main = "Análisis de correspondencia, escalamiento 1"
)

3.2 Ordenación restringida con modelización

A continuación, el análisis de ordenación propiamente. La parte más importante es el entrenamiento: la función train del paquete caret, contenida en la función my_train, simplifica la selección de variables. Lo más importante: prueba con todas las variables primero, observa las variables que recomienda el modelo final (print_my_train(mod)) y ensaya varias combinaciones de subconjuntos de variables.

mc_t_ren <- mc_t %>%
  rename_all(~ paste('ESPECIE', .x))
env_spp <- env %>% bind_cols(mc_t_ren)
spp <- paste0('`', grep('^ESPECIE', colnames(env_spp), value = T), '`', collapse = ' + ')
my_formula <- as.formula(paste(spp, '~ .'))
set.seed(1); mod <- my_train(
  formula = my_formula, 
  # preproceso = 'scale',
  data = env_spp,
  num_variables = 3:4)

Reordering variables and trying again:
Reordering variables and trying again:
Reordering variables and trying again:
Reordering variables and trying again:

print_my_train(mod)

$resumen_variables
Subset selection object
140 Variables  (and intercept)
                                                                        Forced in
`ESA Trees`                                                                 FALSE
`ESA Shrubland`                                                             FALSE
`ESA Grassland`                                                             FALSE
`ESA Cropland`                                                              FALSE
`ESA Built-up`                                                              FALSE
`ESA Barren / sparse vegetation`                                            FALSE
`ESA Open water`                                                            FALSE
`ESA Herbaceous wetland`                                                    FALSE
`CGL Closed forest, evergreen needle leaf`                                  FALSE
`CGL Closed forest, evergreen broad leaf`                                   FALSE
`CGL Closed forest, deciduous broad leaf`                                   FALSE
`CGL Closed forest, mixed`                                                  FALSE
`CGL Closed forest, not matching any of the other definitions`              FALSE
`CGL Open forest, evergreen broad leaf`                                     FALSE
`CGL Open forest, deciduous broad leaf`                                     FALSE
`CGL Open forest, mixed`                                                    FALSE
`CGL Open forest, not matching any of the other definitions`                FALSE
`CGL Shrubs`                                                                FALSE
`CGL Oceans, seas`                                                          FALSE
`CGL Herbaceous vegetation`                                                 FALSE
`CGL Cultivated and managed vegetation / agriculture`                       FALSE
`CGL Urban / built up`                                                      FALSE
`CGL Bare / sparse vegetation`                                              FALSE
`CGL Permanent water bodies`                                                FALSE
`GSL Peak/ridge (warm)`                                                     FALSE
`GSL Peak/ridge`                                                            FALSE
`GSL Mountain/divide`                                                       FALSE
`GSL Cliff`                                                                 FALSE
`GSL Upper slope (warm)`                                                    FALSE
`GSL Upper slope`                                                           FALSE
`GSL Upper slope (cool)`                                                    FALSE
`GSL Upper slope (flat)`                                                    FALSE
`GSL Lower slope (warm)`                                                    FALSE
`GSL Lower slope`                                                           FALSE
`GSL Lower slope (flat)`                                                    FALSE
`GSL Valley`                                                                FALSE
`GHH coefficient_of_variation_1km`                                          FALSE
`GHH contrast_1km`                                                          FALSE
`GHH correlation_1km`                                                       FALSE
`GHH dissimilarity_1km`                                                     FALSE
`GHH entropy_1km`                                                           FALSE
`GHH homogeneity_1km`                                                       FALSE
`GHH maximum_1km`                                                           FALSE
`GHH mean_1km`                                                              FALSE
`GHH pielou_1km`                                                            FALSE
`GHH range_1km`                                                             FALSE
`GHH shannon_1km`                                                           FALSE
`GHH simpson_1km`                                                           FALSE
`GHH standard_deviation_1km`                                                FALSE
`GHH uniformity_1km`                                                        FALSE
`GHH variance_1km`                                                          FALSE
`WCL bio01 Annual mean temperature`                                         FALSE
`WCL bio02 Mean diurnal range mean of monthly max temp - min temp`          FALSE
`WCL bio03 Isothermality bio02 div/bio07`                                   FALSE
`WCL bio04 Temperature seasonality Standard deviation times 100`            FALSE
`WCL bio05 Max temperature of warmest month`                                FALSE
`WCL bio06 Min temperature of coldest month`                                FALSE
`WCL bio08 Mean temperature of wettest quarter`                             FALSE
`WCL bio09 Mean temperature of driest quarter`                              FALSE
`WCL bio10 Mean temperature of warmest quarter`                             FALSE
`WCL bio11 Mean temperature of coldest quarter`                             FALSE
`WCL bio12 Annual precipitation`                                            FALSE
`WCL bio13 Precipitation of wettest month`                                  FALSE
`WCL bio14 Precipitation of driest month`                                   FALSE
`WCL bio15 Precipitation seasonality`                                       FALSE
`WCL bio16 Precipitation of wettest quarter`                                FALSE
`WCL bio17 Precipitation of driest quarter`                                 FALSE
`WCL bio18 Precipitation of warmest quarter`                                FALSE
`WCL bio19 Precipitation of coldest quarter`                                FALSE
`CH-BIO bio01 mean annual air temperature`                                  FALSE
`CH-BIO bio02 mean diurnal air temperature range`                           FALSE
`CH-BIO bio03 isothermality`                                                FALSE
`CH-BIO bio04 temperature seasonality`                                      FALSE
`CH-BIO bio05 mean daily maximum air temperature of the warmest month`      FALSE
`CH-BIO bio06 mean daily minimum air temperature of the coldest month`      FALSE
`ESA Mangroves`                                                             FALSE
`CGL Open forest, evergreen needle leaf`                                    FALSE
`CGL Herbaceous wetland`                                                    FALSE
`GSL Lower slope (cool)`                                                    FALSE
`GSL Valley (narrow)`                                                       FALSE
`WCL bio07 Temperature annual range bio05-bio06`                            FALSE
`CH-BIO bio07 annual range of air temperature`                              FALSE
`CH-BIO bio08 mean daily mean air temperatures of the wettest quarter`      FALSE
`CH-BIO bio09 mean daily mean air temperatures of the driest quarter`       FALSE
`CH-BIO bio10 mean daily mean air temperatures of the warmest quarter`      FALSE
`CH-BIO bio11 mean daily mean air temperatures of the coldest quarter`      FALSE
`CH-BIO bio12 annual precipitation amount`                                  FALSE
`CH-BIO bio13 precipitation amount of the wettest month`                    FALSE
`CH-BIO bio14 precipitation amount of the driest month`                     FALSE
`CH-BIO bio15 precipitation seasonality`                                    FALSE
`CH-BIO bio16 mean monthly precipitation amount of the wettest quarter`     FALSE
`CH-BIO bio17 mean monthly precipitation amount of the driest quarter`      FALSE
`CH-BIO bio18 mean monthly precipitation amount of the warmest quarter`     FALSE
`CH-BIO bio19 mean monthly precipitation amount of the coldest quarter`     FALSE
`G90 Compound Topographic Index`                                            FALSE
`G90 Roughness`                                                             FALSE
`G90 Slope`                                                                 FALSE
`G90 Stream Power Index`                                                    FALSE
`G90 Terrain Ruggedness Index`                                              FALSE
`G90 Topographic Position Index`                                            FALSE
`G90 Vector Ruggedness Measure`                                             FALSE
`G90-GEOM flat`                                                             FALSE
`G90-GEOM pit`                                                              FALSE
`G90-GEOM peak`                                                             FALSE
`G90-GEOM ridge`                                                            FALSE
`G90-GEOM shoulder`                                                         FALSE
`G90-GEOM spur`                                                             FALSE
`G90-GEOM slope`                                                            FALSE
`G90-GEOM hollow`                                                           FALSE
`G90-GEOM footslope`                                                        FALSE
`G90-GEOM valley`                                                           FALSE
`CGIAR-ELE mean`                                                            FALSE
`GFC-PTC YEAR 2000 mean`                                                    FALSE
`GFC-LOSS year 2001`                                                        FALSE
`GFC-LOSS year 2002`                                                        FALSE
`GFC-LOSS year 2003`                                                        FALSE
`GFC-LOSS year 2004`                                                        FALSE
`GFC-LOSS year 2005`                                                        FALSE
`GFC-LOSS year 2006`                                                        FALSE
`GFC-LOSS year 2007`                                                        FALSE
`GFC-LOSS year 2008`                                                        FALSE
`GFC-LOSS year 2009`                                                        FALSE
`GFC-LOSS year 2010`                                                        FALSE
`GFC-LOSS year 2011`                                                        FALSE
`GFC-LOSS year 2012`                                                        FALSE
`GFC-LOSS year 2013`                                                        FALSE
`GFC-LOSS year 2014`                                                        FALSE
`GFC-LOSS year 2015`                                                        FALSE
`GFC-LOSS year 2016`                                                        FALSE
`GFC-LOSS year 2017`                                                        FALSE
`GFC-LOSS year 2018`                                                        FALSE
`GFC-LOSS year 2019`                                                        FALSE
`GFC-LOSS year 2020`                                                        FALSE
`GFC-LOSS year 2021`                                                        FALSE
`OSM-DIST mean`                                                             FALSE
`GP-CONSUNadj YEAR 2020 sum`                                                FALSE
`YINSOLTIME mean`                                                           FALSE
`WBW-DIST mean`                                                             FALSE
`TSEASON-IZZO mean`                                                         FALSE
`PSEASON-IZZO mean`                                                         FALSE
                                                                        Forced out
`ESA Trees`                                                                  FALSE
`ESA Shrubland`                                                              FALSE
`ESA Grassland`                                                              FALSE
`ESA Cropland`                                                               FALSE
`ESA Built-up`                                                               FALSE
`ESA Barren / sparse vegetation`                                             FALSE
`ESA Open water`                                                             FALSE
`ESA Herbaceous wetland`                                                     FALSE
`CGL Closed forest, evergreen needle leaf`                                   FALSE
`CGL Closed forest, evergreen broad leaf`                                    FALSE
`CGL Closed forest, deciduous broad leaf`                                    FALSE
`CGL Closed forest, mixed`                                                   FALSE
`CGL Closed forest, not matching any of the other definitions`               FALSE
`CGL Open forest, evergreen broad leaf`                                      FALSE
`CGL Open forest, deciduous broad leaf`                                      FALSE
`CGL Open forest, mixed`                                                     FALSE
`CGL Open forest, not matching any of the other definitions`                 FALSE
`CGL Shrubs`                                                                 FALSE
`CGL Oceans, seas`                                                           FALSE
`CGL Herbaceous vegetation`                                                  FALSE
`CGL Cultivated and managed vegetation / agriculture`                        FALSE
`CGL Urban / built up`                                                       FALSE
`CGL Bare / sparse vegetation`                                               FALSE
`CGL Permanent water bodies`                                                 FALSE
`GSL Peak/ridge (warm)`                                                      FALSE
`GSL Peak/ridge`                                                             FALSE
`GSL Mountain/divide`                                                        FALSE
`GSL Cliff`                                                                  FALSE
`GSL Upper slope (warm)`                                                     FALSE
`GSL Upper slope`                                                            FALSE
`GSL Upper slope (cool)`                                                     FALSE
`GSL Upper slope (flat)`                                                     FALSE
`GSL Lower slope (warm)`                                                     FALSE
`GSL Lower slope`                                                            FALSE
`GSL Lower slope (flat)`                                                     FALSE
`GSL Valley`                                                                 FALSE
`GHH coefficient_of_variation_1km`                                           FALSE
`GHH contrast_1km`                                                           FALSE
`GHH correlation_1km`                                                        FALSE
`GHH dissimilarity_1km`                                                      FALSE
`GHH entropy_1km`                                                            FALSE
`GHH homogeneity_1km`                                                        FALSE
`GHH maximum_1km`                                                            FALSE
`GHH mean_1km`                                                               FALSE
`GHH pielou_1km`                                                             FALSE
`GHH range_1km`                                                              FALSE
`GHH shannon_1km`                                                            FALSE
`GHH simpson_1km`                                                            FALSE
`GHH standard_deviation_1km`                                                 FALSE
`GHH uniformity_1km`                                                         FALSE
`GHH variance_1km`                                                           FALSE
`WCL bio01 Annual mean temperature`                                          FALSE
`WCL bio02 Mean diurnal range mean of monthly max temp - min temp`           FALSE
`WCL bio03 Isothermality bio02 div/bio07`                                    FALSE
`WCL bio04 Temperature seasonality Standard deviation times 100`             FALSE
`WCL bio05 Max temperature of warmest month`                                 FALSE
`WCL bio06 Min temperature of coldest month`                                 FALSE
`WCL bio08 Mean temperature of wettest quarter`                              FALSE
`WCL bio09 Mean temperature of driest quarter`                               FALSE
`WCL bio10 Mean temperature of warmest quarter`                              FALSE
`WCL bio11 Mean temperature of coldest quarter`                              FALSE
`WCL bio12 Annual precipitation`                                             FALSE
`WCL bio13 Precipitation of wettest month`                                   FALSE
`WCL bio14 Precipitation of driest month`                                    FALSE
`WCL bio15 Precipitation seasonality`                                        FALSE
`WCL bio16 Precipitation of wettest quarter`                                 FALSE
`WCL bio17 Precipitation of driest quarter`                                  FALSE
`WCL bio18 Precipitation of warmest quarter`                                 FALSE
`WCL bio19 Precipitation of coldest quarter`                                 FALSE
`CH-BIO bio01 mean annual air temperature`                                   FALSE
`CH-BIO bio02 mean diurnal air temperature range`                            FALSE
`CH-BIO bio03 isothermality`                                                 FALSE
`CH-BIO bio04 temperature seasonality`                                       FALSE
`CH-BIO bio05 mean daily maximum air temperature of the warmest month`       FALSE
`CH-BIO bio06 mean daily minimum air temperature of the coldest month`       FALSE
`ESA Mangroves`                                                              FALSE
`CGL Open forest, evergreen needle leaf`                                     FALSE
`CGL Herbaceous wetland`                                                     FALSE
`GSL Lower slope (cool)`                                                     FALSE
`GSL Valley (narrow)`                                                        FALSE
`WCL bio07 Temperature annual range bio05-bio06`                             FALSE
`CH-BIO bio07 annual range of air temperature`                               FALSE
`CH-BIO bio08 mean daily mean air temperatures of the wettest quarter`       FALSE
`CH-BIO bio09 mean daily mean air temperatures of the driest quarter`        FALSE
`CH-BIO bio10 mean daily mean air temperatures of the warmest quarter`       FALSE
`CH-BIO bio11 mean daily mean air temperatures of the coldest quarter`       FALSE
`CH-BIO bio12 annual precipitation amount`                                   FALSE
`CH-BIO bio13 precipitation amount of the wettest month`                     FALSE
`CH-BIO bio14 precipitation amount of the driest month`                      FALSE
`CH-BIO bio15 precipitation seasonality`                                     FALSE
`CH-BIO bio16 mean monthly precipitation amount of the wettest quarter`      FALSE
`CH-BIO bio17 mean monthly precipitation amount of the driest quarter`       FALSE
`CH-BIO bio18 mean monthly precipitation amount of the warmest quarter`      FALSE
`CH-BIO bio19 mean monthly precipitation amount of the coldest quarter`      FALSE
`G90 Compound Topographic Index`                                             FALSE
`G90 Roughness`                                                              FALSE
`G90 Slope`                                                                  FALSE
`G90 Stream Power Index`                                                     FALSE
`G90 Terrain Ruggedness Index`                                               FALSE
`G90 Topographic Position Index`                                             FALSE
`G90 Vector Ruggedness Measure`                                              FALSE
`G90-GEOM flat`                                                              FALSE
`G90-GEOM pit`                                                               FALSE
`G90-GEOM peak`                                                              FALSE
`G90-GEOM ridge`                                                             FALSE
`G90-GEOM shoulder`                                                          FALSE
`G90-GEOM spur`                                                              FALSE
`G90-GEOM slope`                                                             FALSE
`G90-GEOM hollow`                                                            FALSE
`G90-GEOM footslope`                                                         FALSE
`G90-GEOM valley`                                                            FALSE
`CGIAR-ELE mean`                                                             FALSE
`GFC-PTC YEAR 2000 mean`                                                     FALSE
`GFC-LOSS year 2001`                                                         FALSE
`GFC-LOSS year 2002`                                                         FALSE
`GFC-LOSS year 2003`                                                         FALSE
`GFC-LOSS year 2004`                                                         FALSE
`GFC-LOSS year 2005`                                                         FALSE
`GFC-LOSS year 2006`                                                         FALSE
`GFC-LOSS year 2007`                                                         FALSE
`GFC-LOSS year 2008`                                                         FALSE
`GFC-LOSS year 2009`                                                         FALSE
`GFC-LOSS year 2010`                                                         FALSE
`GFC-LOSS year 2011`                                                         FALSE
`GFC-LOSS year 2012`                                                         FALSE
`GFC-LOSS year 2013`                                                         FALSE
`GFC-LOSS year 2014`                                                         FALSE
`GFC-LOSS year 2015`                                                         FALSE
`GFC-LOSS year 2016`                                                         FALSE
`GFC-LOSS year 2017`                                                         FALSE
`GFC-LOSS year 2018`                                                         FALSE
`GFC-LOSS year 2019`                                                         FALSE
`GFC-LOSS year 2020`                                                         FALSE
`GFC-LOSS year 2021`                                                         FALSE
`OSM-DIST mean`                                                              FALSE
`GP-CONSUNadj YEAR 2020 sum`                                                 FALSE
`YINSOLTIME mean`                                                            FALSE
`WBW-DIST mean`                                                              FALSE
`TSEASON-IZZO mean`                                                          FALSE
`PSEASON-IZZO mean`                                                          FALSE
1 subsets of each size up to 4
Selection Algorithm: 'sequential replacement'
         `ESA Trees` `ESA Shrubland` `ESA Grassland` `ESA Cropland`
1  ( 1 ) " "         " "             " "             " "           
2  ( 1 ) " "         " "             " "             " "           
3  ( 1 ) " "         " "             " "             " "           
4  ( 1 ) " "         " "             " "             " "           
         `ESA Built-up` `ESA Barren / sparse vegetation` `ESA Open water`
1  ( 1 ) " "            " "                              " "             
2  ( 1 ) " "            " "                              " "             
3  ( 1 ) " "            " "                              " "             
4  ( 1 ) "*"            " "                              " "             
         `ESA Herbaceous wetland` `ESA Mangroves`
1  ( 1 ) " "                      " "            
2  ( 1 ) " "                      " "            
3  ( 1 ) " "                      " "            
4  ( 1 ) " "                      " "            
         `CGL Closed forest, evergreen needle leaf`
1  ( 1 ) " "                                       
2  ( 1 ) " "                                       
3  ( 1 ) " "                                       
4  ( 1 ) " "                                       
         `CGL Closed forest, evergreen broad leaf`
1  ( 1 ) " "                                      
2  ( 1 ) " "                                      
3  ( 1 ) "*"                                      
4  ( 1 ) "*"                                      
         `CGL Closed forest, deciduous broad leaf` `CGL Closed forest, mixed`
1  ( 1 ) " "                                       " "                       
2  ( 1 ) " "                                       " "                       
3  ( 1 ) " "                                       " "                       
4  ( 1 ) " "                                       " "                       
         `CGL Closed forest, not matching any of the other definitions`
1  ( 1 ) " "                                                           
2  ( 1 ) " "                                                           
3  ( 1 ) " "                                                           
4  ( 1 ) " "                                                           
         `CGL Open forest, evergreen needle leaf`
1  ( 1 ) " "                                     
2  ( 1 ) " "                                     
3  ( 1 ) " "                                     
4  ( 1 ) " "                                     
         `CGL Open forest, evergreen broad leaf`
1  ( 1 ) " "                                    
2  ( 1 ) " "                                    
3  ( 1 ) " "                                    
4  ( 1 ) " "                                    
         `CGL Open forest, deciduous broad leaf` `CGL Open forest, mixed`
1  ( 1 ) " "                                     " "                     
2  ( 1 ) " "                                     " "                     
3  ( 1 ) " "                                     " "                     
4  ( 1 ) " "                                     " "                     
         `CGL Open forest, not matching any of the other definitions`
1  ( 1 ) " "                                                         
2  ( 1 ) " "                                                         
3  ( 1 ) " "                                                         
4  ( 1 ) " "                                                         
         `CGL Shrubs` `CGL Oceans, seas` `CGL Herbaceous vegetation`
1  ( 1 ) " "          " "                " "                        
2  ( 1 ) " "          " "                " "                        
3  ( 1 ) " "          " "                " "                        
4  ( 1 ) " "          " "                " "                        
         `CGL Cultivated and managed vegetation / agriculture`
1  ( 1 ) " "                                                  
2  ( 1 ) " "                                                  
3  ( 1 ) " "                                                  
4  ( 1 ) " "                                                  
         `CGL Urban / built up` `CGL Bare / sparse vegetation`
1  ( 1 ) "*"                    " "                           
2  ( 1 ) "*"                    " "                           
3  ( 1 ) "*"                    " "                           
4  ( 1 ) " "                    " "                           
         `CGL Permanent water bodies` `CGL Herbaceous wetland`
1  ( 1 ) " "                          " "                     
2  ( 1 ) " "                          " "                     
3  ( 1 ) " "                          " "                     
4  ( 1 ) " "                          " "                     
         `GSL Peak/ridge (warm)` `GSL Peak/ridge` `GSL Mountain/divide`
1  ( 1 ) " "                     " "              " "                  
2  ( 1 ) " "                     " "              " "                  
3  ( 1 ) " "                     " "              " "                  
4  ( 1 ) " "                     " "              " "                  
         `GSL Cliff` `GSL Upper slope (warm)` `GSL Upper slope`
1  ( 1 ) " "         " "                      " "              
2  ( 1 ) " "         " "                      " "              
3  ( 1 ) " "         " "                      " "              
4  ( 1 ) " "         " "                      " "              
         `GSL Upper slope (cool)` `GSL Upper slope (flat)`
1  ( 1 ) " "                      " "                     
2  ( 1 ) " "                      " "                     
3  ( 1 ) " "                      " "                     
4  ( 1 ) " "                      " "                     
         `GSL Lower slope (warm)` `GSL Lower slope` `GSL Lower slope (cool)`
1  ( 1 ) " "                      " "               " "                     
2  ( 1 ) " "                      " "               " "                     
3  ( 1 ) " "                      " "               " "                     
4  ( 1 ) " "                      " "               " "                     
         `GSL Lower slope (flat)` `GSL Valley` `GSL Valley (narrow)`
1  ( 1 ) " "                      " "          " "                  
2  ( 1 ) " "                      " "          " "                  
3  ( 1 ) " "                      " "          " "                  
4  ( 1 ) " "                      " "          " "                  
         `GHH coefficient_of_variation_1km` `GHH contrast_1km`
1  ( 1 ) " "                                " "               
2  ( 1 ) " "                                " "               
3  ( 1 ) " "                                " "               
4  ( 1 ) " "                                " "               
         `GHH correlation_1km` `GHH dissimilarity_1km` `GHH entropy_1km`
1  ( 1 ) " "                   " "                     " "              
2  ( 1 ) " "                   " "                     " "              
3  ( 1 ) " "                   " "                     " "              
4  ( 1 ) " "                   " "                     " "              
         `GHH homogeneity_1km` `GHH maximum_1km` `GHH mean_1km`
1  ( 1 ) " "                   " "               " "           
2  ( 1 ) " "                   " "               " "           
3  ( 1 ) " "                   " "               " "           
4  ( 1 ) " "                   " "               " "           
         `GHH pielou_1km` `GHH range_1km` `GHH shannon_1km` `GHH simpson_1km`
1  ( 1 ) " "              " "             " "               " "              
2  ( 1 ) " "              " "             " "               " "              
3  ( 1 ) " "              " "             " "               " "              
4  ( 1 ) " "              " "             " "               " "              
         `GHH standard_deviation_1km` `GHH uniformity_1km` `GHH variance_1km`
1  ( 1 ) " "                          " "                  " "               
2  ( 1 ) " "                          " "                  " "               
3  ( 1 ) " "                          " "                  " "               
4  ( 1 ) " "                          " "                  " "               
         `WCL bio01 Annual mean temperature`
1  ( 1 ) " "                                
2  ( 1 ) " "                                
3  ( 1 ) " "                                
4  ( 1 ) " "                                
         `WCL bio02 Mean diurnal range mean of monthly max temp - min temp`
1  ( 1 ) " "                                                               
2  ( 1 ) " "                                                               
3  ( 1 ) " "                                                               
4  ( 1 ) " "                                                               
         `WCL bio03 Isothermality bio02 div/bio07`
1  ( 1 ) " "                                      
2  ( 1 ) " "                                      
3  ( 1 ) " "                                      
4  ( 1 ) " "                                      
         `WCL bio04 Temperature seasonality Standard deviation times 100`
1  ( 1 ) " "                                                             
2  ( 1 ) " "                                                             
3  ( 1 ) " "                                                             
4  ( 1 ) " "                                                             
         `WCL bio05 Max temperature of warmest month`
1  ( 1 ) " "                                         
2  ( 1 ) " "                                         
3  ( 1 ) " "                                         
4  ( 1 ) " "                                         
         `WCL bio06 Min temperature of coldest month`
1  ( 1 ) " "                                         
2  ( 1 ) " "                                         
3  ( 1 ) " "                                         
4  ( 1 ) " "                                         
         `WCL bio07 Temperature annual range bio05-bio06`
1  ( 1 ) " "                                             
2  ( 1 ) " "                                             
3  ( 1 ) " "                                             
4  ( 1 ) " "                                             
         `WCL bio08 Mean temperature of wettest quarter`
1  ( 1 ) " "                                            
2  ( 1 ) " "                                            
3  ( 1 ) " "                                            
4  ( 1 ) " "                                            
         `WCL bio09 Mean temperature of driest quarter`
1  ( 1 ) " "                                           
2  ( 1 ) " "                                           
3  ( 1 ) " "                                           
4  ( 1 ) " "                                           
         `WCL bio10 Mean temperature of warmest quarter`
1  ( 1 ) " "                                            
2  ( 1 ) " "                                            
3  ( 1 ) " "                                            
4  ( 1 ) " "                                            
         `WCL bio11 Mean temperature of coldest quarter`
1  ( 1 ) " "                                            
2  ( 1 ) " "                                            
3  ( 1 ) " "                                            
4  ( 1 ) " "                                            
         `WCL bio12 Annual precipitation`
1  ( 1 ) " "                             
2  ( 1 ) " "                             
3  ( 1 ) " "                             
4  ( 1 ) " "                             
         `WCL bio13 Precipitation of wettest month`
1  ( 1 ) " "                                       
2  ( 1 ) " "                                       
3  ( 1 ) " "                                       
4  ( 1 ) " "                                       
         `WCL bio14 Precipitation of driest month`
1  ( 1 ) " "                                      
2  ( 1 ) " "                                      
3  ( 1 ) " "                                      
4  ( 1 ) "*"                                      
         `WCL bio15 Precipitation seasonality`
1  ( 1 ) " "                                  
2  ( 1 ) " "                                  
3  ( 1 ) " "                                  
4  ( 1 ) " "                                  
         `WCL bio16 Precipitation of wettest quarter`
1  ( 1 ) " "                                         
2  ( 1 ) " "                                         
3  ( 1 ) " "                                         
4  ( 1 ) " "                                         
         `WCL bio17 Precipitation of driest quarter`
1  ( 1 ) " "                                        
2  ( 1 ) " "                                        
3  ( 1 ) " "                                        
4  ( 1 ) " "                                        
         `WCL bio18 Precipitation of warmest quarter`
1  ( 1 ) " "                                         
2  ( 1 ) " "                                         
3  ( 1 ) " "                                         
4  ( 1 ) " "                                         
         `WCL bio19 Precipitation of coldest quarter`
1  ( 1 ) " "                                         
2  ( 1 ) "*"                                         
3  ( 1 ) "*"                                         
4  ( 1 ) "*"                                         
         `CH-BIO bio01 mean annual air temperature`
1  ( 1 ) " "                                       
2  ( 1 ) " "                                       
3  ( 1 ) " "                                       
4  ( 1 ) " "                                       
         `CH-BIO bio02 mean diurnal air temperature range`
1  ( 1 ) " "                                              
2  ( 1 ) " "                                              
3  ( 1 ) " "                                              
4  ( 1 ) " "                                              
         `CH-BIO bio03 isothermality` `CH-BIO bio04 temperature seasonality`
1  ( 1 ) " "                          " "                                   
2  ( 1 ) " "                          " "                                   
3  ( 1 ) " "                          " "                                   
4  ( 1 ) " "                          " "                                   
         `CH-BIO bio05 mean daily maximum air temperature of the warmest month`
1  ( 1 ) " "                                                                   
2  ( 1 ) " "                                                                   
3  ( 1 ) " "                                                                   
4  ( 1 ) " "                                                                   
         `CH-BIO bio06 mean daily minimum air temperature of the coldest month`
1  ( 1 ) " "                                                                   
2  ( 1 ) " "                                                                   
3  ( 1 ) " "                                                                   
4  ( 1 ) " "                                                                   
         `CH-BIO bio07 annual range of air temperature`
1  ( 1 ) " "                                           
2  ( 1 ) " "                                           
3  ( 1 ) " "                                           
4  ( 1 ) " "                                           
         `CH-BIO bio08 mean daily mean air temperatures of the wettest quarter`
1  ( 1 ) " "                                                                   
2  ( 1 ) " "                                                                   
3  ( 1 ) " "                                                                   
4  ( 1 ) " "                                                                   
         `CH-BIO bio09 mean daily mean air temperatures of the driest quarter`
1  ( 1 ) " "                                                                  
2  ( 1 ) " "                                                                  
3  ( 1 ) " "                                                                  
4  ( 1 ) " "                                                                  
         `CH-BIO bio10 mean daily mean air temperatures of the warmest quarter`
1  ( 1 ) " "                                                                   
2  ( 1 ) " "                                                                   
3  ( 1 ) " "                                                                   
4  ( 1 ) " "                                                                   
         `CH-BIO bio11 mean daily mean air temperatures of the coldest quarter`
1  ( 1 ) " "                                                                   
2  ( 1 ) " "                                                                   
3  ( 1 ) " "                                                                   
4  ( 1 ) " "                                                                   
         `CH-BIO bio12 annual precipitation amount`
1  ( 1 ) " "                                       
2  ( 1 ) " "                                       
3  ( 1 ) " "                                       
4  ( 1 ) " "                                       
         `CH-BIO bio13 precipitation amount of the wettest month`
1  ( 1 ) " "                                                     
2  ( 1 ) " "                                                     
3  ( 1 ) " "                                                     
4  ( 1 ) " "                                                     
         `CH-BIO bio14 precipitation amount of the driest month`
1  ( 1 ) " "                                                    
2  ( 1 ) " "                                                    
3  ( 1 ) " "                                                    
4  ( 1 ) " "                                                    
         `CH-BIO bio15 precipitation seasonality`
1  ( 1 ) " "                                     
2  ( 1 ) " "                                     
3  ( 1 ) " "                                     
4  ( 1 ) " "                                     
         `CH-BIO bio16 mean monthly precipitation amount of the wettest quarter`
1  ( 1 ) " "                                                                    
2  ( 1 ) " "                                                                    
3  ( 1 ) " "                                                                    
4  ( 1 ) " "                                                                    
         `CH-BIO bio17 mean monthly precipitation amount of the driest quarter`
1  ( 1 ) " "                                                                   
2  ( 1 ) " "                                                                   
3  ( 1 ) " "                                                                   
4  ( 1 ) " "                                                                   
         `CH-BIO bio18 mean monthly precipitation amount of the warmest quarter`
1  ( 1 ) " "                                                                    
2  ( 1 ) " "                                                                    
3  ( 1 ) " "                                                                    
4  ( 1 ) " "                                                                    
         `CH-BIO bio19 mean monthly precipitation amount of the coldest quarter`
1  ( 1 ) " "                                                                    
2  ( 1 ) " "                                                                    
3  ( 1 ) " "                                                                    
4  ( 1 ) " "                                                                    
         `G90 Compound Topographic Index` `G90 Roughness` `G90 Slope`
1  ( 1 ) " "                              " "             " "        
2  ( 1 ) " "                              " "             " "        
3  ( 1 ) " "                              " "             " "        
4  ( 1 ) " "                              " "             " "        
         `G90 Stream Power Index` `G90 Terrain Ruggedness Index`
1  ( 1 ) " "                      " "                           
2  ( 1 ) " "                      " "                           
3  ( 1 ) " "                      " "                           
4  ( 1 ) " "                      " "                           
         `G90 Topographic Position Index` `G90 Vector Ruggedness Measure`
1  ( 1 ) " "                              " "                            
2  ( 1 ) " "                              " "                            
3  ( 1 ) " "                              " "                            
4  ( 1 ) " "                              " "                            
         `G90-GEOM flat` `G90-GEOM pit` `G90-GEOM peak` `G90-GEOM ridge`
1  ( 1 ) " "             " "            " "             " "             
2  ( 1 ) " "             " "            " "             " "             
3  ( 1 ) " "             " "            " "             " "             
4  ( 1 ) " "             " "            " "             " "             
         `G90-GEOM shoulder` `G90-GEOM spur` `G90-GEOM slope` `G90-GEOM hollow`
1  ( 1 ) " "                 " "             " "              " "              
2  ( 1 ) " "                 " "             " "              " "              
3  ( 1 ) " "                 " "             " "              " "              
4  ( 1 ) " "                 " "             " "              " "              
         `G90-GEOM footslope` `G90-GEOM valley` `CGIAR-ELE mean`
1  ( 1 ) " "                  " "               " "             
2  ( 1 ) " "                  " "               " "             
3  ( 1 ) " "                  " "               " "             
4  ( 1 ) " "                  " "               " "             
         `GFC-PTC YEAR 2000 mean` `GFC-LOSS year 2001` `GFC-LOSS year 2002`
1  ( 1 ) " "                      " "                  " "                 
2  ( 1 ) " "                      " "                  " "                 
3  ( 1 ) " "                      " "                  " "                 
4  ( 1 ) " "                      " "                  " "                 
         `GFC-LOSS year 2003` `GFC-LOSS year 2004` `GFC-LOSS year 2005`
1  ( 1 ) " "                  " "                  " "                 
2  ( 1 ) " "                  " "                  " "                 
3  ( 1 ) " "                  " "                  " "                 
4  ( 1 ) " "                  " "                  " "                 
         `GFC-LOSS year 2006` `GFC-LOSS year 2007` `GFC-LOSS year 2008`
1  ( 1 ) " "                  " "                  " "                 
2  ( 1 ) " "                  " "                  " "                 
3  ( 1 ) " "                  " "                  " "                 
4  ( 1 ) " "                  " "                  " "                 
         `GFC-LOSS year 2009` `GFC-LOSS year 2010` `GFC-LOSS year 2011`
1  ( 1 ) " "                  " "                  " "                 
2  ( 1 ) " "                  " "                  " "                 
3  ( 1 ) " "                  " "                  " "                 
4  ( 1 ) " "                  " "                  " "                 
         `GFC-LOSS year 2012` `GFC-LOSS year 2013` `GFC-LOSS year 2014`
1  ( 1 ) " "                  " "                  " "                 
2  ( 1 ) " "                  " "                  " "                 
3  ( 1 ) " "                  " "                  " "                 
4  ( 1 ) " "                  " "                  " "                 
         `GFC-LOSS year 2015` `GFC-LOSS year 2016` `GFC-LOSS year 2017`
1  ( 1 ) " "                  " "                  " "                 
2  ( 1 ) " "                  " "                  " "                 
3  ( 1 ) " "                  " "                  " "                 
4  ( 1 ) " "                  " "                  " "                 
         `GFC-LOSS year 2018` `GFC-LOSS year 2019` `GFC-LOSS year 2020`
1  ( 1 ) " "                  " "                  " "                 
2  ( 1 ) " "                  " "                  " "                 
3  ( 1 ) " "                  " "                  " "                 
4  ( 1 ) " "                  " "                  " "                 
         `GFC-LOSS year 2021` `OSM-DIST mean` `GP-CONSUNadj YEAR 2020 sum`
1  ( 1 ) " "                  " "             " "                         
2  ( 1 ) " "                  " "             " "                         
3  ( 1 ) " "                  " "             " "                         
4  ( 1 ) " "                  " "             " "                         
         `YINSOLTIME mean` `WBW-DIST mean` `TSEASON-IZZO mean`
1  ( 1 ) " "               " "             " "                
2  ( 1 ) " "               " "             " "                
3  ( 1 ) " "               " "             " "                
4  ( 1 ) " "               " "             " "                
         `PSEASON-IZZO mean`
1  ( 1 ) " "                
2  ( 1 ) " "                
3  ( 1 ) " "                
4  ( 1 ) " "                

$resultados_nvmax
  nvmax      RMSE   Rsquared       MAE     RMSESD RsquaredSD      MAESD
1     3 0.4735272 0.15957658 0.3593891 0.03013711 0.15785964 0.01415654
2     4 0.5930253 0.07616854 0.4696069 0.08681543 0.08374517 0.04557660

$mejor_ajuste
  nvmax
1     3

(covar <- grep(
  pattern = '\\(Intercept\\)',
  x = names(coef(mod$finalModel,unlist(mod$bestTune))),
  invert = T, value = T))

[1] "`CGL Closed forest, deciduous broad leaf`"                             
[2] "`CGL Permanent water bodies`"                                          
[3] "`CH-BIO bio06 mean daily minimum air temperature of the coldest month`"

rda_mc_t <- rda(mc_t_ren %>% rename_all(~ gsub('^ESPECIE ', '', .)) ~ .,
                    env %>% select_at(all_of(gsub('\\`', '', covar))), scale = T)

A continuación, el resumen del análisis de redundancia.

summary(rda_mc_t)


Call:
rda(formula = mc_t_ren %>% rename_all(~gsub("^ESPECIE ", "",      .)) ~ `CGL Closed forest, deciduous broad leaf` + `CGL Permanent water bodies` +      `CH-BIO bio06 mean daily minimum air temperature of the coldest month`,      data = env %>% select_at(all_of(gsub("\\`", "", covar))),      scale = T) 

Partitioning of correlations:
              Inertia Proportion
Total          41.000    1.00000
Constrained     2.655    0.06475
Unconstrained  38.345    0.93525

Eigenvalues, and their contribution to the correlations 

Importance of components:
                         RDA1    RDA2    RDA3     PC1     PC2     PC3     PC4
Eigenvalue            1.47322 0.67638 0.50502 2.27459 2.24795 2.04413 1.91120
Proportion Explained  0.03593 0.01650 0.01232 0.05548 0.05483 0.04986 0.04661
Cumulative Proportion 0.03593 0.05243 0.06475 0.12022 0.17505 0.22491 0.27152
                          PC5     PC6     PC7     PC8    PC9    PC10    PC11
Eigenvalue            1.81410 1.78265 1.68189 1.60983 1.5743 1.49871 1.39354
Proportion Explained  0.04425 0.04348 0.04102 0.03926 0.0384 0.03655 0.03399
Cumulative Proportion 0.31577 0.35925 0.40027 0.43954 0.4779 0.51449 0.54847
                         PC12    PC13    PC14    PC15    PC16    PC17    PC18
Eigenvalue            1.37452 1.25900 1.18318 1.10992 1.07744 1.04249 1.01090
Proportion Explained  0.03352 0.03071 0.02886 0.02707 0.02628 0.02543 0.02466
Cumulative Proportion 0.58200 0.61271 0.64156 0.66864 0.69491 0.72034 0.74500
                         PC19    PC20    PC21    PC22    PC23    PC24    PC25
Eigenvalue            0.98906 0.87596 0.83911 0.81108 0.79106 0.73185 0.66991
Proportion Explained  0.02412 0.02136 0.02047 0.01978 0.01929 0.01785 0.01634
Cumulative Proportion 0.76912 0.79049 0.81095 0.83073 0.85003 0.86788 0.88422
                         PC26    PC27   PC28    PC29    PC30     PC31     PC32
Eigenvalue            0.60646 0.50728 0.4960 0.47674 0.42154 0.363377 0.319653
Proportion Explained  0.01479 0.01237 0.0121 0.01163 0.01028 0.008863 0.007796
Cumulative Proportion 0.89901 0.91138 0.9235 0.93511 0.94539 0.954251 0.962048
                          PC33     PC34     PC35     PC36     PC37     PC38
Eigenvalue            0.299177 0.278864 0.239975 0.188253 0.146885 0.117103
Proportion Explained  0.007297 0.006802 0.005853 0.004592 0.003583 0.002856
Cumulative Proportion 0.969345 0.976146 0.981999 0.986591 0.990173 0.993029
                          PC39     PC40     PC41
Eigenvalue            0.114081 0.102978 0.068736
Proportion Explained  0.002782 0.002512 0.001676
Cumulative Proportion 0.995812 0.998324 1.000000

Accumulated constrained eigenvalues
Importance of components:
                       RDA1   RDA2   RDA3
Eigenvalue            1.473 0.6764 0.5050
Proportion Explained  0.555 0.2548 0.1902
Cumulative Proportion 0.555 0.8098 1.0000

La varianza ajustada explicada por el modelo.

RsquareAdj(rda_mc_t)$adj.r.squared

[1] 0.02782866

Y el factor de inflación de la varianza.

vif.cca(rda_mc_t)

                             `CGL Closed forest, deciduous broad leaf` 
                                                              1.026777 
                                          `CGL Permanent water bodies` 
                                                              1.090118 
`CH-BIO bio06 mean daily minimum air temperature of the coldest month` 
                                                              1.099702

Represento el gráfico triplot.

# Triplot
escalado <- 1
plot(rda_mc_t,
     scaling = escalado,
     display = c("sp", "lc", "cn"),
     main = paste("Triplot de RDA especies ~ variables, escalamiento", escalado)
)
rda_mc_t_sc1 <- scores(rda_mc_t,
         choices = 1:2,
         scaling = escalado,
         display = "sp"
  )
# text(mi_fam_t_rda, "species", col="red", cex=0.8, scaling=escalado)
arrows(0, 0,
       rda_mc_t_sc1[, 1] * 0.9,
       rda_mc_t_sc1[, 2] * 0.9,
       length = 0,
       lty = 1,
       col = "red"
)

4 Análisis de diversidad + análisis de agrupamiento abreviado

Me basaré en los scripts que comienzan por di_ de este repo, los cuales explico en los vídeos de “Análisis de diversidad” (vídeos 19 y 20) de la lista de reproducción “Ecología Numérica con R” de mi canal. Dichos vídeos tienen aplicaciones ligeramente diferentes, pues los datos fuente usados en ellos son de abundancia, mientras que los tuyos son de presencia/ausencia.

4.1 Calcular riqueza (e índices)

La principal desventaja de trabajar con registros de presencia, es que la mayoría de los índices de diversidad alpha fueron diseñados originalmente para calcularse a partir de datos de abundancia. Sin embargo, la riqueza de especies, que es el número \(q=0\) de Hill (\(=N_0\) en las columnas que produce la función alpha_div) es un buen proxy sobre la diversidad, y nos ayudará a comparar sitios.

Además de la columna N0 del objeto que generaré en el bloque siguiente, verás que la función alpha_div genera otras columnas; son índices pensados para datos de abundancia, que en este caso no usaremos, pero los muestro para que tengas una visión completa del análisis de diversidad con índices que podría serte de utilidad en el futuro.

Por otra parte, afortunadamente, los métodos de estimación de riqueza de Chao, y los de diversidad beta (al final de esta sección), aprovechan sustancialmente los registros de presencia/ausencia para realizar estimaciones consistentes y fiables.

Una nota adicional. En el análisis de diversidad, es útil (no imprescindible) disponer de un análisis clúster (agrupamiento) básico. Este te servirá para comparar la riqueza observada y la esperada entre hábitats. Por esta razón, combinamos análisis de diversidad con agrupamiento. Sin embargo, si el análisis de agrupamiento generó grupos de dos o menos elementos, dicha comparación no será realizable.

indices <- alpha_div(mc) %>% 
  mutate(sitio = rownames(.)) %>% 
  relocate(sitio, .before = everything())

El objeto mc es la matriz de comunidad de presecia/ausencia. La función alpha_div es un “envoltorio” generado por mí para calcular múltiples índices de diversidad y estimaciones, basada en las funciones de los paquetes SpadeR y iNEXT. Si usásemos datos de abundancia, los índices que calcula la función “alpha_div” serían útiles, pero con registros de presencia/ausencia, como es nuestro caso, sólo la columna N0 (riqueza) nos aportará algún resultado con sentido.

indices %>% 
  kable(booktabs=T) %>%
  kable_styling(latex_options = c("HOLD_position", "scale_down")) %>%
  gsub(' NA |NaN ', '', .) #Lista de especies

	sitio	N0	H	Hb2	N1	N1b2	N2	J	E10	E20
854c8997fffffff	854c8997fffffff	11	2.3978953	3.459432	11	11	11	1	1	1
854cd29bfffffff	854cd29bfffffff	5	1.6094379	2.321928	5	5	5	1	1	1
854cd477fffffff	854cd477fffffff	4	1.3862944	2.000000	4	4	4	1	1	1
854cd46bfffffff	854cd46bfffffff	2	0.6931472	1.000000	2	2	2	1	1	1
854c8927fffffff	854c8927fffffff	6	1.7917595	2.584963	6	6	6	1	1	1
854c89b3fffffff	854c89b3fffffff	5	1.6094379	2.321928	5	5	5	1	1	1
854cd4cbfffffff	854cd4cbfffffff	10	2.3025851	3.321928	10	10	10	1	1	1
854cd40bfffffff	854cd40bfffffff	4	1.3862944	2.000000	4	4	4	1	1	1
854cd423fffffff	854cd423fffffff	6	1.7917595	2.584963	6	6	6	1	1	1
854cc6c7fffffff	854cc6c7fffffff	3	1.0986123	1.584963	3	3	3	1	1	1
854c892ffffffff	854c892ffffffff	4	1.3862944	2.000000	4	4	4	1	1	1
854cd63bfffffff	854cd63bfffffff	2	0.6931472	1.000000	2	2	2	1	1	1
854cd457fffffff	854cd457fffffff	5	1.6094379	2.321928	5	5	5	1	1	1
856725b7fffffff	856725b7fffffff	4	1.3862944	2.000000	4	4	4	1	1	1
854cf26ffffffff	854cf26ffffffff	6	1.7917595	2.584963	6	6	6	1	1	1
85672537fffffff	85672537fffffff	2	0.6931472	1.000000	2	2	2	1	1	1
854c89abfffffff	854c89abfffffff	3	1.0986123	1.584963	3	3	3	1	1	1
854c89a3fffffff	854c89a3fffffff	6	1.7917595	2.584963	6	6	6	1	1	1
854cd647fffffff	854cd647fffffff	2	0.6931472	1.000000	2	2	2	1	1	1
854cd453fffffff	854cd453fffffff	7	1.9459101	2.807355	7	7	7	1	1	1
854cf373fffffff	854cf373fffffff	3	1.0986123	1.584963	3	3	3	1	1	1
854c89c7fffffff	854c89c7fffffff	2	0.6931472	1.000000	2	2	2	1	1	1
854c89c3fffffff	854c89c3fffffff	3	1.0986123	1.584963	3	3	3	1	1	1
854cf31bfffffff	854cf31bfffffff	5	1.6094379	2.321928	5	5	5	1	1	1
854cf243fffffff	854cf243fffffff	11	2.3978953	3.459432	11	11	11	1	1	1
854cd0c3fffffff	854cd0c3fffffff	2	0.6931472	1.000000	2	2	2	1	1	1
854cd42ffffffff	854cd42ffffffff	10	2.3025851	3.321928	10	10	10	1	1	1
854cf347fffffff	854cf347fffffff	3	1.0986123	1.584963	3	3	3	1	1	1
854cf303fffffff	854cf303fffffff	3	1.0986123	1.584963	3	3	3	1	1	1
854cd5b3fffffff	854cd5b3fffffff	4	1.3862944	2.000000	4	4	4	1	1	1
854cc64ffffffff	854cc64ffffffff	2	0.6931472	1.000000	2	2	2	1	1	1
854c893bfffffff	854c893bfffffff	3	1.0986123	1.584963	3	3	3	1	1	1
854cf267fffffff	854cf267fffffff	2	0.6931472	1.000000	2	2	2	1	1	1
854cf333fffffff	854cf333fffffff	2	0.6931472	1.000000	2	2	2	1	1	1
854cf26bfffffff	854cf26bfffffff	2	0.6931472	1.000000	2	2	2	1	1	1
854cd427fffffff	854cd427fffffff	8	2.0794415	3.000000	8	8	8	1	1	1
854c894bfffffff	854c894bfffffff	2	0.6931472	1.000000	2	2	2	1	1	1
854cf20ffffffff	854cf20ffffffff	3	1.0986123	1.584963	3	3	3	1	1	1
854cc6cffffffff	854cc6cffffffff	4	1.3862944	2.000000	4	4	4	1	1	1
854cc603fffffff	854cc603fffffff	3	1.0986123	1.584963	3	3	3	1	1	1
854cc613fffffff	854cc613fffffff	4	1.3862944	2.000000	4	4	4	1	1	1
854cd513fffffff	854cd513fffffff	4	1.3862944	2.000000	4	4	4	1	1	1
854cd5b7fffffff	854cd5b7fffffff	7	1.9459101	2.807355	7	7	7	1	1	1
854cf353fffffff	854cf353fffffff	2	0.6931472	1.000000	2	2	2	1	1	1
854cd403fffffff	854cd403fffffff	2	0.6931472	1.000000	2	2	2	1	1	1
854cd093fffffff	854cd093fffffff	2	0.6931472	1.000000	2	2	2	1	1	1
854c89a7fffffff	854c89a7fffffff	3	1.0986123	1.584963	3	3	3	1	1	1
854cd553fffffff	854cd553fffffff	2	0.6931472	1.000000	2	2	2	1	1	1
854cf24bfffffff	854cf24bfffffff	3	1.0986123	1.584963	3	3	3	1	1	1
854cd09bfffffff	854cd09bfffffff	4	1.3862944	2.000000	4	4	4	1	1	1
854cd653fffffff	854cd653fffffff	2	0.6931472	1.000000	2	2	2	1	1	1
854cd46ffffffff	854cd46ffffffff	3	1.0986123	1.584963	3	3	3	1	1	1
854cd4d3fffffff	854cd4d3fffffff	3	1.0986123	1.584963	3	3	3	1	1	1
854cc657fffffff	854cc657fffffff	3	1.0986123	1.584963	3	3	3	1	1	1
854cd41bfffffff	854cd41bfffffff	3	1.0986123	1.584963	3	3	3	1	1	1
854cd293fffffff	854cd293fffffff	3	1.0986123	1.584963	3	3	3	1	1	1
854c8933fffffff	854c8933fffffff	3	1.0986123	1.584963	3	3	3	1	1	1
854cd667fffffff	854cd667fffffff	5	1.6094379	2.321928	5	5	5	1	1	1
854c898ffffffff	854c898ffffffff	8	2.0794415	3.000000	8	8	8	1	1	1
854cd2d7fffffff	854cd2d7fffffff	3	1.0986123	1.584963	3	3	3	1	1	1
854c89bbfffffff	854c89bbfffffff	2	0.6931472	1.000000	2	2	2	1	1	1
854cd40ffffffff	854cd40ffffffff	2	0.6931472	1.000000	2	2	2	1	1	1
854cc67bfffffff	854cc67bfffffff	2	0.6931472	1.000000	2	2	2	1	1	1
854cd467fffffff	854cd467fffffff	4	1.3862944	2.000000	4	4	4	1	1	1
854cf323fffffff	854cf323fffffff	3	1.0986123	1.584963	3	3	3	1	1	1
854c89b7fffffff	854c89b7fffffff	2	0.6931472	1.000000	2	2	2	1	1	1
854cd5cffffffff	854cd5cffffffff	2	0.6931472	1.000000	2	2	2	1	1	1
854cd693fffffff	854cd693fffffff	2	0.6931472	1.000000	2	2	2	1	1	1
854cf343fffffff	854cf343fffffff	3	1.0986123	1.584963	3	3	3	1	1	1
854cd47bfffffff	854cd47bfffffff	2	0.6931472	1.000000	2	2	2	1	1	1
854cd623fffffff	854cd623fffffff	3	1.0986123	1.584963	3	3	3	1	1	1
854cd473fffffff	854cd473fffffff	3	1.0986123	1.584963	3	3	3	1	1	1
854c890ffffffff	854c890ffffffff	2	0.6931472	1.000000	2	2	2	1	1	1
854cd4c3fffffff	854cd4c3fffffff	2	0.6931472	1.000000	2	2	2	1	1	1
854cc673fffffff	854cc673fffffff	2	0.6931472	1.000000	2	2	2	1	1	1
854cd66ffffffff	854cd66ffffffff	2	0.6931472	1.000000	2	2	2	1	1	1
854cd697fffffff	854cd697fffffff	3	1.0986123	1.584963	3	3	3	1	1	1
854cd6affffffff	854cd6affffffff	2	0.6931472	1.000000	2	2	2	1	1	1
854cd58bfffffff	854cd58bfffffff	3	1.0986123	1.584963	3	3	3	1	1	1
854cd44bfffffff	854cd44bfffffff	2	0.6931472	1.000000	2	2	2	1	1	1

Los sitios ordenados en función de su riqueza:

indices %>%
  arrange(desc(N0)) %>% 
  kable(booktabs=T) %>%
  kable_styling(latex_options = c("HOLD_position", "scale_down")) %>%
  gsub(' NA |NaN ', '', .) #Lista de especies

	sitio	N0	H	Hb2	N1	N1b2	N2	J	E10	E20
854c8997fffffff	854c8997fffffff	11	2.3978953	3.459432	11	11	11	1	1	1
854cf243fffffff	854cf243fffffff	11	2.3978953	3.459432	11	11	11	1	1	1
854cd4cbfffffff	854cd4cbfffffff	10	2.3025851	3.321928	10	10	10	1	1	1
854cd42ffffffff	854cd42ffffffff	10	2.3025851	3.321928	10	10	10	1	1	1
854cd427fffffff	854cd427fffffff	8	2.0794415	3.000000	8	8	8	1	1	1
854c898ffffffff	854c898ffffffff	8	2.0794415	3.000000	8	8	8	1	1	1
854cd453fffffff	854cd453fffffff	7	1.9459101	2.807355	7	7	7	1	1	1
854cd5b7fffffff	854cd5b7fffffff	7	1.9459101	2.807355	7	7	7	1	1	1
854c8927fffffff	854c8927fffffff	6	1.7917595	2.584963	6	6	6	1	1	1
854cd423fffffff	854cd423fffffff	6	1.7917595	2.584963	6	6	6	1	1	1
854cf26ffffffff	854cf26ffffffff	6	1.7917595	2.584963	6	6	6	1	1	1
854c89a3fffffff	854c89a3fffffff	6	1.7917595	2.584963	6	6	6	1	1	1
854cd29bfffffff	854cd29bfffffff	5	1.6094379	2.321928	5	5	5	1	1	1
854c89b3fffffff	854c89b3fffffff	5	1.6094379	2.321928	5	5	5	1	1	1
854cd457fffffff	854cd457fffffff	5	1.6094379	2.321928	5	5	5	1	1	1
854cf31bfffffff	854cf31bfffffff	5	1.6094379	2.321928	5	5	5	1	1	1
854cd667fffffff	854cd667fffffff	5	1.6094379	2.321928	5	5	5	1	1	1
854cd477fffffff	854cd477fffffff	4	1.3862944	2.000000	4	4	4	1	1	1
854cd40bfffffff	854cd40bfffffff	4	1.3862944	2.000000	4	4	4	1	1	1
854c892ffffffff	854c892ffffffff	4	1.3862944	2.000000	4	4	4	1	1	1
856725b7fffffff	856725b7fffffff	4	1.3862944	2.000000	4	4	4	1	1	1
854cd5b3fffffff	854cd5b3fffffff	4	1.3862944	2.000000	4	4	4	1	1	1
854cc6cffffffff	854cc6cffffffff	4	1.3862944	2.000000	4	4	4	1	1	1
854cc613fffffff	854cc613fffffff	4	1.3862944	2.000000	4	4	4	1	1	1
854cd513fffffff	854cd513fffffff	4	1.3862944	2.000000	4	4	4	1	1	1
854cd09bfffffff	854cd09bfffffff	4	1.3862944	2.000000	4	4	4	1	1	1
854cd467fffffff	854cd467fffffff	4	1.3862944	2.000000	4	4	4	1	1	1
854cc6c7fffffff	854cc6c7fffffff	3	1.0986123	1.584963	3	3	3	1	1	1
854c89abfffffff	854c89abfffffff	3	1.0986123	1.584963	3	3	3	1	1	1
854cf373fffffff	854cf373fffffff	3	1.0986123	1.584963	3	3	3	1	1	1
854c89c3fffffff	854c89c3fffffff	3	1.0986123	1.584963	3	3	3	1	1	1
854cf347fffffff	854cf347fffffff	3	1.0986123	1.584963	3	3	3	1	1	1
854cf303fffffff	854cf303fffffff	3	1.0986123	1.584963	3	3	3	1	1	1
854c893bfffffff	854c893bfffffff	3	1.0986123	1.584963	3	3	3	1	1	1
854cf20ffffffff	854cf20ffffffff	3	1.0986123	1.584963	3	3	3	1	1	1
854cc603fffffff	854cc603fffffff	3	1.0986123	1.584963	3	3	3	1	1	1
854c89a7fffffff	854c89a7fffffff	3	1.0986123	1.584963	3	3	3	1	1	1
854cf24bfffffff	854cf24bfffffff	3	1.0986123	1.584963	3	3	3	1	1	1
854cd46ffffffff	854cd46ffffffff	3	1.0986123	1.584963	3	3	3	1	1	1
854cd4d3fffffff	854cd4d3fffffff	3	1.0986123	1.584963	3	3	3	1	1	1
854cc657fffffff	854cc657fffffff	3	1.0986123	1.584963	3	3	3	1	1	1
854cd41bfffffff	854cd41bfffffff	3	1.0986123	1.584963	3	3	3	1	1	1
854cd293fffffff	854cd293fffffff	3	1.0986123	1.584963	3	3	3	1	1	1
854c8933fffffff	854c8933fffffff	3	1.0986123	1.584963	3	3	3	1	1	1
854cd2d7fffffff	854cd2d7fffffff	3	1.0986123	1.584963	3	3	3	1	1	1
854cf323fffffff	854cf323fffffff	3	1.0986123	1.584963	3	3	3	1	1	1
854cf343fffffff	854cf343fffffff	3	1.0986123	1.584963	3	3	3	1	1	1
854cd623fffffff	854cd623fffffff	3	1.0986123	1.584963	3	3	3	1	1	1
854cd473fffffff	854cd473fffffff	3	1.0986123	1.584963	3	3	3	1	1	1
854cd697fffffff	854cd697fffffff	3	1.0986123	1.584963	3	3	3	1	1	1
854cd58bfffffff	854cd58bfffffff	3	1.0986123	1.584963	3	3	3	1	1	1
854cd46bfffffff	854cd46bfffffff	2	0.6931472	1.000000	2	2	2	1	1	1
854cd63bfffffff	854cd63bfffffff	2	0.6931472	1.000000	2	2	2	1	1	1
85672537fffffff	85672537fffffff	2	0.6931472	1.000000	2	2	2	1	1	1
854cd647fffffff	854cd647fffffff	2	0.6931472	1.000000	2	2	2	1	1	1
854c89c7fffffff	854c89c7fffffff	2	0.6931472	1.000000	2	2	2	1	1	1
854cd0c3fffffff	854cd0c3fffffff	2	0.6931472	1.000000	2	2	2	1	1	1
854cc64ffffffff	854cc64ffffffff	2	0.6931472	1.000000	2	2	2	1	1	1
854cf267fffffff	854cf267fffffff	2	0.6931472	1.000000	2	2	2	1	1	1
854cf333fffffff	854cf333fffffff	2	0.6931472	1.000000	2	2	2	1	1	1
854cf26bfffffff	854cf26bfffffff	2	0.6931472	1.000000	2	2	2	1	1	1
854c894bfffffff	854c894bfffffff	2	0.6931472	1.000000	2	2	2	1	1	1
854cf353fffffff	854cf353fffffff	2	0.6931472	1.000000	2	2	2	1	1	1
854cd403fffffff	854cd403fffffff	2	0.6931472	1.000000	2	2	2	1	1	1
854cd093fffffff	854cd093fffffff	2	0.6931472	1.000000	2	2	2	1	1	1
854cd553fffffff	854cd553fffffff	2	0.6931472	1.000000	2	2	2	1	1	1
854cd653fffffff	854cd653fffffff	2	0.6931472	1.000000	2	2	2	1	1	1
854c89bbfffffff	854c89bbfffffff	2	0.6931472	1.000000	2	2	2	1	1	1
854cd40ffffffff	854cd40ffffffff	2	0.6931472	1.000000	2	2	2	1	1	1
854cc67bfffffff	854cc67bfffffff	2	0.6931472	1.000000	2	2	2	1	1	1
854c89b7fffffff	854c89b7fffffff	2	0.6931472	1.000000	2	2	2	1	1	1
854cd5cffffffff	854cd5cffffffff	2	0.6931472	1.000000	2	2	2	1	1	1
854cd693fffffff	854cd693fffffff	2	0.6931472	1.000000	2	2	2	1	1	1
854cd47bfffffff	854cd47bfffffff	2	0.6931472	1.000000	2	2	2	1	1	1
854c890ffffffff	854c890ffffffff	2	0.6931472	1.000000	2	2	2	1	1	1
854cd4c3fffffff	854cd4c3fffffff	2	0.6931472	1.000000	2	2	2	1	1	1
854cc673fffffff	854cc673fffffff	2	0.6931472	1.000000	2	2	2	1	1	1
854cd66ffffffff	854cd66ffffffff	2	0.6931472	1.000000	2	2	2	1	1	1
854cd6affffffff	854cd6affffffff	2	0.6931472	1.000000	2	2	2	1	1	1
854cd44bfffffff	854cd44bfffffff	2	0.6931472	1.000000	2	2	2	1	1	1

4.2 Evaluar correlación entre riqueza y variables ambientales mediante matriz de correlación.

En el bloque siguiente, represento gráficamente la correlación entre la riqueza y las variables ambientales mediante un panel de gráficos, que suele llamarse también “matriz de correlación”, expresada gráficamente. Si usases índices de diversidad, como el de Shannon o los números de Hill, también deberías incluirlos en el gráfico; nota que en este ejemplo, sólo uso la riqueza (la función select(N0) se encarga de conservar sólo la riqueza). Esto es lo que debes saber sobre el panel:

Presta atención a la primera columna y la primera fila de la matriz, que muestra cómo se correlaciona N0 con las variables ambientales que elijas.
La diagonal contiene gráficos de línea que muestra la densidad de la variable en cuestión.
Los gráficos del “triángulo superior”, y que contienen el patrón Corr: ####, muestran el valor del coeficiente de correlación de Pearson (\(r\)) entre las variables intersectadas. Si existe un \(|r|\) elevado (es decir, si es muy cercano a -1 o a 1) y la prueba de producto-momento es significativa (si hay uno o varios asteriscos, o un punto, lo es), entonces toma nota de que dicha variable se asocia estadísticamente con la riqueza. Si \(r\) es negativo, la relación es inversa (cuando aumenta la variable, disminuye la riqueza, y viceversa); si es positivo, la relación es directa (cuando aumenta la variable, aumenta también la riqueza).
En el “triángulo inferior”, que es un espejo del superior, se sitúan los gráficos de dispersión de las variables intersectadas. Si los puntos siguen un patrón de distribución formando una elipse imaginaria (organizados en torno a una línea recta imaginaria inclinada), entonces existe correlación.

bind_cols(indices %>% select(N0), env %>%
            select(matches('^ESA')) %>% 
            rename_with(.fn = ~ paste0('AMB_', .))) %>%
  ggpairs(
    labeller = label_wrap_gen(width=10),
    upper = list(continuous = GGally::wrap("cor", size = 3))) +
  theme(text = element_text(size = 10))

Nota que el panel de correlación anterior sólo incluye un grupo de variables ambientales. Son muchas variables ambientales, y si se incluyen todas, el servidor no aguantaría. Es por ello que ves el comando select(matches('^ESA')), el cual selecciona las columnas que comienzan por la cadena de caracteres ‘ESA’. Como bien explique arriba, ‘ESA’ se refiere a los datos de cobertura del suelo de la Agencia Espacial Europea (ESA 2021, imágenes Sentinel 1 y Sentinel 2 a 10 m de resolucion máximo). Se deben hacer más paneles de correlación para otros grupos de variables, ver la lista de grupos posibles en la columna “Sufijo del grupo” de la Tabla 1 de este HTML. Otros grupos posibles: CH-BIO, CGL, G90, WCL, entre muchos otros.

4.3 “Completitud de muestra” y curva de acumulación

“Completitud”, en porcentajes, según distintos estimadores. Con un 80% de completitud, se considera en general una muestra representativa. Sin embargo, este umbral de 80% no debe tomarse de forma estricta. Sobre todo porque existen métodos refinados que mejoran las estimaciones

riqueza_estimaciones <- data.frame(specpool(mc) %>% select(-matches('.se$'))) %>% 
  select(`Riqueza observada` = Species,
         `Número de sitios` = n,
         `Estimación por Chao (clásico)` = chao,
         `Estimación por jackknife de primer orden` = jack1,
         `Estimación por jackknife de segundo orden` = jack2,
         `Estimación por bootstrap` = boot) %>% 
  pivot_longer(cols = everything(), names_to = 'Variable', values_to = 'Valor') %>%
  mutate(`Cobertura (%)` = Valor / (filter(., Variable == "Riqueza observada") %>% pull(Valor)) * 100) %>% 
  mutate(`Cobertura (%)` = ifelse(Variable %in% c('Riqueza observada', 'Número de sitios'), NA, `Cobertura (%)`))
riqueza_estimaciones %>% estilo_kable(alinear = 'lrr')

Table 4.1:
Variable	Valor	Cobertura (%)
Riqueza observada	41.00
Número de sitios	80.00
Estimación por Chao (clásico)	41.56	101.35
Estimación por jackknife de primer orden	43.96	107.23
Estimación por jackknife de segundo orden	39.19	95.58
Estimación por bootstrap	43.44	105.95

# Bug no resuelto:
# Error in if (var_mle > 0) { : valor ausente donde TRUE/FALSE es necesario
# Varios intentos frustrados por lograr que funcione. Entiendo que el problema
# está en el número de doubletons (la matriz no tiene), pero no logré mejorar
# la función interna SpecInciHomo para solucionarlo. La versión de SpadeR usada
# en la aplicación Shiny https://chao.shinyapps.io/SpadeR/, no es la misma que 
# la que se encuentra en GitHub ni en el CRAN, pues esa no tiene bug.
df_spader <- data.frame(V1 = as.integer(c(nrow(mc), colSums(mc))))
# También se puede crear con esta línea:
# df_spader <- structure(
#   list(V1 = c(15, 8, 9, 10, 9, 8, 9, 9, 8, 8, 6, 12, 9)),
#   class = "data.frame", row.names = c(NA, -13L))
df_spader
#  V1
#  15
#   8
#   9
#  10
#   9
#   8
#   9
#   9
#   8
#   8
#   6
#  12
#   9
ChaoSpecies(df_spader, datatype = 'incidence_freq',
            k = min(df_spader$V1), conf=0.95)
# Error in if (var_mle > 0) { : valor ausente donde TRUE/FALSE es necesario
# ENG: Error in if (var_mle > 0) { : missing value where TRUE/FALSE needed

Graficaré la curva de acumulación de especies.

mc_general <- mc %>%
  summarise_all(sum) %>%
  mutate(N = nrow(mc)) %>%
  relocate(N, .before = 1) %>%
  data.frame
nasin_raref <- iNEXT::iNEXT(
  x = t(mc_general),
  q=0,
  knots = 2000,
  datatype = 'incidence_freq')
acumulacion_especies <- iNEXT::ggiNEXT(nasin_raref, type=1) +
  theme_bw() +
  theme(
    text = element_text(size = 20),
    panel.background = element_rect(fill = 'white', colour = 'black'),
    panel.grid.major = element_line(colour = "grey", linetype = "dashed", size = 0.25)
  ) +
  ylab('Riqueza de especies') +
  xlab('Número de sitios') +
  scale_y_continuous(breaks = seq(0, 80, length.out = 9)) +
  scale_color_manual(values = brewer.pal(8, 'Set2')) +
  scale_fill_manual(values = brewer.pal(8, 'Set2'))
acumulacion_especies

Ahora según los grupos previamente seleccionados en el análisis de agrupamiento.

grupos_seleccionados <- readRDS('grupos_seleccionados-Acanthaceae.RDS')
mc_grupos <- mc %>%
  mutate(g = grupos_seleccionados) %>%
  group_by(g) %>%
  summarise_all(sum) %>%
  select(-g) %>% 
  mutate(N = nrow(mc)) %>% 
  relocate(N, .before = 1) %>% 
  data.frame
nasin_raref_general <- iNEXT::iNEXT(
  x = t(mc_grupos),
  q=0,
  knots = 400,
  datatype = 'incidence_freq')
acumulacion_especies_grupos <- iNEXT::ggiNEXT(nasin_raref_general, type=1) +
  theme_bw() +
  theme(
    text = element_text(size = 20),
    panel.background = element_rect(fill = 'white', colour = 'black'),
    panel.grid.major = element_line(colour = "grey", linetype = "dashed", size = 0.25)
  ) +
  ylab('Riqueza de especies') +
  xlab('Número de sitios') +
  scale_y_continuous(breaks = seq(0, 80, length.out = 9)) +
  scale_color_manual(values = brewer.pal(8, 'Set2')) +
  scale_fill_manual(values = brewer.pal(8, 'Set2'))
acumulacion_especies_grupos

4.4 Contribución de especies a la diversidad beta (SCBD, species contribution to beta diversity) y contribución local a la diversidad beta (LCBD local contribution to beta diversity)

determinar_contrib_local_y_especie(
    mc = mc,
    alpha = 0.05,
    nperm = 9999,
    metodo = 'sorensen')

## $betadiv
## $beta
##   SStotal   BDtotal 
## 34.284186  0.433977 
## 
## $SCBD
## [1] NA
## 
## $LCBD
## 854c8997fffffff 854cd29bfffffff 854cd477fffffff 854cd46bfffffff 854c8927fffffff 
##     0.011055196     0.013721213     0.011266000     0.015160955     0.010815415 
## 854c89b3fffffff 854cd4cbfffffff 854cd40bfffffff 854cd423fffffff 854cc6c7fffffff 
##     0.010145092     0.010643130     0.012813865     0.011375810     0.011287399 
## 854c892ffffffff 854cd63bfffffff 854cd457fffffff 856725b7fffffff 854cf26ffffffff 
##     0.011701573     0.015919135     0.013845306     0.012860190     0.008606514 
## 85672537fffffff 854c89abfffffff 854c89a3fffffff 854cd647fffffff 854cd453fffffff 
##     0.012962985     0.011619493     0.010241650     0.011156815     0.011651166 
## 854cf373fffffff 854c89c7fffffff 854c89c3fffffff 854cf31bfffffff 854cf243fffffff 
##     0.011262091     0.013181745     0.014735002     0.012168373     0.011579610 
## 854cd0c3fffffff 854cd42ffffffff 854cf347fffffff 854cf303fffffff 854cd5b3fffffff 
##     0.015304525     0.010098212     0.014491654     0.011619493     0.011249507 
## 854cc64ffffffff 854c893bfffffff 854cf267fffffff 854cf333fffffff 854cf26bfffffff 
##     0.013547502     0.011505772     0.015376821     0.011421049     0.014085520 
## 854cd427fffffff 854c894bfffffff 854cf20ffffffff 854cc6cffffffff 854cc603fffffff 
##     0.010373761     0.014230358     0.012451643     0.010062243     0.011945975 
## 854cc613fffffff 854cd513fffffff 854cd5b7fffffff 854cf353fffffff 854cd403fffffff 
##     0.009917850     0.012419145     0.011924595     0.012741332     0.014094156 
## 854cd093fffffff 854c89a7fffffff 854cd553fffffff 854cf24bfffffff 854cd09bfffffff 
##     0.010631328     0.011472504     0.011156815     0.011379328     0.009795791 
## 854cd653fffffff 854cd46ffffffff 854cd4d3fffffff 854cc657fffffff 854cd41bfffffff 
##     0.012818637     0.014058231     0.011635865     0.012688904     0.010528543 
## 854cd293fffffff 854c8933fffffff 854cd667fffffff 854c898ffffffff 854cd2d7fffffff 
##     0.012159166     0.013611680     0.014466787     0.010701041     0.012552903 
## 854c89bbfffffff 854cd40ffffffff 854cc67bfffffff 854cd467fffffff 854cf323fffffff 
##     0.013552421     0.011830456     0.015329699     0.011604031     0.010722418 
## 854c89b7fffffff 854cd5cffffffff 854cd693fffffff 854cf343fffffff 854cd47bfffffff 
##     0.014579911     0.014829143     0.015800162     0.014460737     0.011304970 
## 854cd623fffffff 854cd473fffffff 854c890ffffffff 854cd4c3fffffff 854cc673fffffff 
##     0.015469120     0.013954683     0.015415307     0.011421049     0.012316054 
## 854cd66ffffffff 854cd697fffffff 854cd6affffffff 854cd58bfffffff 854cd44bfffffff 
##     0.012277725     0.014459282     0.014936119     0.010185909     0.015256446 
## 
## $p.LCBD
## 854c8997fffffff 854cd29bfffffff 854cd477fffffff 854cd46bfffffff 854c8927fffffff 
##          0.4424          0.1118          0.4145          0.0349          0.4657 
## 854c89b3fffffff 854cd4cbfffffff 854cd40bfffffff 854cd423fffffff 854cc6c7fffffff 
##          0.5180          0.4839          0.2207          0.4021          0.4199 
## 854c892ffffffff 854cd63bfffffff 854cd457fffffff 856725b7fffffff 854cf26ffffffff 
##          0.3638          0.0097          0.1056          0.2177          0.5543 
## 85672537fffffff 854c89abfffffff 854c89a3fffffff 854cd647fffffff 854cd453fffffff 
##          0.1963          0.3725          0.5131          0.4369          0.3717 
## 854cf373fffffff 854c89c7fffffff 854c89c3fffffff 854cf31bfffffff 854cf243fffffff 
##          0.4231          0.1732          0.0507          0.3100          0.3887 
## 854cd0c3fffffff 854cd42ffffffff 854cf347fffffff 854cf303fffffff 854cd5b3fffffff 
##          0.0306          0.5225          0.0672          0.3753          0.4220 
## 854cc64ffffffff 854c893bfffffff 854cf267fffffff 854cf333fffffff 854cf26bfffffff 
##          0.1379          0.3964          0.0284          0.4012          0.0900 
## 854cd427fffffff 854c894bfffffff 854cf20ffffffff 854cc6cffffffff 854cc603fffffff 
##          0.5110          0.0791          0.2714          0.5291          0.3436 
## 854cc613fffffff 854cd513fffffff 854cd5b7fffffff 854cf353fffffff 854cd403fffffff 
##          0.5310          0.2800          0.3489          0.2419          0.0945 
## 854cd093fffffff 854c89a7fffffff 854cd553fffffff 854cf24bfffffff 854cd09bfffffff 
##          0.4951          0.4032          0.4417          0.4130          0.5362 
## 854cd653fffffff 854cd46ffffffff 854cd4d3fffffff 854cc657fffffff 854cd41bfffffff 
##          0.2353          0.0982          0.3923          0.2475          0.5005 
## 854cd293fffffff 854c8933fffffff 854cd667fffffff 854c898ffffffff 854cd2d7fffffff 
##          0.3213          0.1407          0.0714          0.4899          0.2700 
## 854c89bbfffffff 854cd40ffffffff 854cc67bfffffff 854cd467fffffff 854cf323fffffff 
##          0.1472          0.3673          0.0367          0.3931          0.4848 
## 854c89b7fffffff 854cd5cffffffff 854cd693fffffff 854cf343fffffff 854cd47bfffffff 
##          0.0658          0.0590          0.0143          0.0650          0.4240 
## 854cd623fffffff 854cd473fffffff 854c890ffffffff 854cd4c3fffffff 854cc673fffffff 
##          0.0246          0.1044          0.0274          0.4026          0.2938 
## 854cd66ffffffff 854cd697fffffff 854cd6affffffff 854cd58bfffffff 854cd44bfffffff 
##          0.3004          0.0671          0.0495          0.5201          0.0378 
## 
## $p.adj
## 854c8997fffffff 854cd29bfffffff 854cd477fffffff 854cd46bfffffff 854c8927fffffff 
##           1.000           1.000           1.000           1.000           1.000 
## 854c89b3fffffff 854cd4cbfffffff 854cd40bfffffff 854cd423fffffff 854cc6c7fffffff 
##           1.000           1.000           1.000           1.000           1.000 
## 854c892ffffffff 854cd63bfffffff 854cd457fffffff 856725b7fffffff 854cf26ffffffff 
##           1.000           0.776           1.000           1.000           1.000 
## 85672537fffffff 854c89abfffffff 854c89a3fffffff 854cd647fffffff 854cd453fffffff 
##           1.000           1.000           1.000           1.000           1.000 
## 854cf373fffffff 854c89c7fffffff 854c89c3fffffff 854cf31bfffffff 854cf243fffffff 
##           1.000           1.000           1.000           1.000           1.000 
## 854cd0c3fffffff 854cd42ffffffff 854cf347fffffff 854cf303fffffff 854cd5b3fffffff 
##           1.000           1.000           1.000           1.000           1.000 
## 854cc64ffffffff 854c893bfffffff 854cf267fffffff 854cf333fffffff 854cf26bfffffff 
##           1.000           1.000           1.000           1.000           1.000 
## 854cd427fffffff 854c894bfffffff 854cf20ffffffff 854cc6cffffffff 854cc603fffffff 
##           1.000           1.000           1.000           1.000           1.000 
## 854cc613fffffff 854cd513fffffff 854cd5b7fffffff 854cf353fffffff 854cd403fffffff 
##           1.000           1.000           1.000           1.000           1.000 
## 854cd093fffffff 854c89a7fffffff 854cd553fffffff 854cf24bfffffff 854cd09bfffffff 
##           1.000           1.000           1.000           1.000           1.000 
## 854cd653fffffff 854cd46ffffffff 854cd4d3fffffff 854cc657fffffff 854cd41bfffffff 
##           1.000           1.000           1.000           1.000           1.000 
## 854cd293fffffff 854c8933fffffff 854cd667fffffff 854c898ffffffff 854cd2d7fffffff 
##           1.000           1.000           1.000           1.000           1.000 
## 854c89bbfffffff 854cd40ffffffff 854cc67bfffffff 854cd467fffffff 854cf323fffffff 
##           1.000           1.000           1.000           1.000           1.000 
## 854c89b7fffffff 854cd5cffffffff 854cd693fffffff 854cf343fffffff 854cd47bfffffff 
##           1.000           1.000           1.000           1.000           1.000 
## 854cd623fffffff 854cd473fffffff 854c890ffffffff 854cd4c3fffffff 854cc673fffffff 
##           1.000           1.000           1.000           1.000           1.000 
## 854cd66ffffffff 854cd697fffffff 854cd6affffffff 854cd58bfffffff 854cd44bfffffff 
##           1.000           1.000           1.000           1.000           1.000 
## 
## $method
## [1] "sorensen"     "sqrt.D=FALSE"
## 
## $note
## [1] "Info -- D is Euclidean because beta.div outputs D[jk] = sqrt(1-S[jk])"
## [2] "For this D functions, use beta.div with option sqrt.D=FALSE"          
## 
## $D
## [1] NA
## 
## attr(,"class")
## [1] "beta.div"
## 
## $especies_contribuyen_betadiv
## [1] NA
## 
## $sitios_contribuyen_betadiv
##  [1] "854cd46bfffffff" "854cd63bfffffff" "854cd0c3fffffff" "854cf267fffffff"
##  [5] "854cc67bfffffff" "854cd693fffffff" "854cd623fffffff" "854c890ffffffff"
##  [9] "854cd6affffffff" "854cd44bfffffff"
## 
## $valor_de_ajustado_lcbd
## 854c8997fffffff 854cd29bfffffff 854cd477fffffff 854cd46bfffffff 854c8927fffffff 
##           1.000           1.000           1.000           1.000           1.000 
## 854c89b3fffffff 854cd4cbfffffff 854cd40bfffffff 854cd423fffffff 854cc6c7fffffff 
##           1.000           1.000           1.000           1.000           1.000 
## 854c892ffffffff 854cd63bfffffff 854cd457fffffff 856725b7fffffff 854cf26ffffffff 
##           1.000           0.776           1.000           1.000           1.000 
## 85672537fffffff 854c89abfffffff 854c89a3fffffff 854cd647fffffff 854cd453fffffff 
##           1.000           1.000           1.000           1.000           1.000 
## 854cf373fffffff 854c89c7fffffff 854c89c3fffffff 854cf31bfffffff 854cf243fffffff 
##           1.000           1.000           1.000           1.000           1.000 
## 854cd0c3fffffff 854cd42ffffffff 854cf347fffffff 854cf303fffffff 854cd5b3fffffff 
##           1.000           1.000           1.000           1.000           1.000 
## 854cc64ffffffff 854c893bfffffff 854cf267fffffff 854cf333fffffff 854cf26bfffffff 
##           1.000           1.000           1.000           1.000           1.000 
## 854cd427fffffff 854c894bfffffff 854cf20ffffffff 854cc6cffffffff 854cc603fffffff 
##           1.000           1.000           1.000           1.000           1.000 
## 854cc613fffffff 854cd513fffffff 854cd5b7fffffff 854cf353fffffff 854cd403fffffff 
##           1.000           1.000           1.000           1.000           1.000 
## 854cd093fffffff 854c89a7fffffff 854cd553fffffff 854cf24bfffffff 854cd09bfffffff 
##           1.000           1.000           1.000           1.000           1.000 
## 854cd653fffffff 854cd46ffffffff 854cd4d3fffffff 854cc657fffffff 854cd41bfffffff 
##           1.000           1.000           1.000           1.000           1.000 
## 854cd293fffffff 854c8933fffffff 854cd667fffffff 854c898ffffffff 854cd2d7fffffff 
##           1.000           1.000           1.000           1.000           1.000 
## 854c89bbfffffff 854cd40ffffffff 854cc67bfffffff 854cd467fffffff 854cf323fffffff 
##           1.000           1.000           1.000           1.000           1.000 
## 854c89b7fffffff 854cd5cffffffff 854cd693fffffff 854cf343fffffff 854cd47bfffffff 
##           1.000           1.000           1.000           1.000           1.000 
## 854cd623fffffff 854cd473fffffff 854c890ffffffff 854cd4c3fffffff 854cc673fffffff 
##           1.000           1.000           1.000           1.000           1.000 
## 854cd66ffffffff 854cd697fffffff 854cd6affffffff 854cd58bfffffff 854cd44bfffffff 
##           1.000           1.000           1.000           1.000           1.000 
## 
## $sitios_contribuyen_betadiv_ajustado
## character(0)

Referencias

Borcard, Daniel, François Gillet, y Pierre Legendre. 2018. Numerical ecology with R. Springer.

Análisis para Práctica de desarrollo 2. PD02. Técnicas de ecología numérica, reproducibilidad

Elaborado por: José-Ramón Martínez-Batlle (jmartinez19@uasd.edu.do)
Facultad de Ciencias, Universidad Autónoma de Santo Domingo (UASD)
Santo Domingo, República Dominicana

0.1 Carga de paquetes

1 Análisis exploratorio de datos (AED)

1.1 Cargar la matriz de comunidad

1.2 Transformar la matriz de comunidad

1.3 Cargar la matriz ambiental

2 Análisis de agrupamiento

2.1 Generación de árboles

2.2 Anchura de siluetas

2.2.1 Anchuras de siluetas para método UPGMA

2.2.2 Anchuras de siluetas para método Ward

2.3 Remuestreo por bootstrap multiescalar

2.4 Conclusión sobre selección de método de agrupamiento y número de grupos

2.5 Grupos (clústers), variables ambientales

2.6 Especies con preferencia/fidelidad con grupos (clústers)

3 Técnicas de ordenación

3.1 Ordenación no restringida

3.1.1 PCA aplicado a datos de comunidad transformados

3.1.2 Análisis de correspondencia (CA)

3.2 Ordenación restringida con modelización

4 Análisis de diversidad + análisis de agrupamiento abreviado

4.1 Calcular riqueza (e índices)

4.2 Evaluar correlación entre riqueza y variables ambientales mediante matriz de correlación.

4.3 “Completitud de muestra” y curva de acumulación

4.4 Contribución de especies a la diversidad beta (SCBD, species contribution to beta diversity) y contribución local a la diversidad beta (LCBD local contribution to beta diversity)

Referencias

Análisis para Práctica de desarrollo 2. PD02. Técnicas de ecología numérica, reproducibilidad

Elaborado por: José-Ramón Martínez-Batlle (jmartinez19@uasd.edu.do) Facultad de Ciencias, Universidad Autónoma de Santo Domingo (UASD) Santo Domingo, República Dominicana

0.1 Carga de paquetes

1 Análisis exploratorio de datos (AED)

1.1 Cargar la matriz de comunidad

1.2 Transformar la matriz de comunidad

1.3 Cargar la matriz ambiental

2 Análisis de agrupamiento

2.1 Generación de árboles

2.2 Anchura de siluetas

2.2.1 Anchuras de siluetas para método UPGMA

2.2.2 Anchuras de siluetas para método Ward

2.3 Remuestreo por bootstrap multiescalar

2.4 Conclusión sobre selección de método de agrupamiento y número de grupos

2.5 Grupos (clústers), variables ambientales

2.6 Especies con preferencia/fidelidad con grupos (clústers)

3 Técnicas de ordenación

3.1 Ordenación no restringida

3.1.1 PCA aplicado a datos de comunidad transformados

3.1.2 Análisis de correspondencia (CA)

3.2 Ordenación restringida con modelización

4 Análisis de diversidad + análisis de agrupamiento abreviado

4.1 Calcular riqueza (e índices)

4.2 Evaluar correlación entre riqueza y variables ambientales mediante matriz de correlación.

4.3 “Completitud de muestra” y curva de acumulación

4.4 Contribución de especies a la diversidad beta (SCBD, species contribution to beta diversity) y contribución local a la diversidad beta (LCBD local contribution to beta diversity)

Referencias

Elaborado por: José-Ramón Martínez-Batlle (jmartinez19@uasd.edu.do)
Facultad de Ciencias, Universidad Autónoma de Santo Domingo (UASD)
Santo Domingo, República Dominicana